def build_pipeline():
pipe = Pipeline("my_pipeline")
pipe.originate(
name="create_three_new_files",
task_func=create_new_file,
output=[os.path.join(WORK_DIR, f"file{i}.csv") for i in range(1, 4)],
)
pipe.transform(
name="convert_csv_files_to_tsv",
task_func=csv_to_tsv,
input=output_from("create_three_new_files"),
filter=suffix(".csv"),
output=".tsv",
)
pipe.transform(
name="calculate_md5",
task_func=md5,
input=output_from("convert_csv_files_to_tsv"),
filter=suffix(".tsv"),
output=".md5sum",
)
return pipe
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='ovarian_cancer_pipeline')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
human_reference_genome_file = state.config.get_option('human_reference_genome')
# Stages are dependent on the state
stages = PipelineStages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(
task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# The human reference genome in FASTA format
pipeline.originate(
task_func=stages.human_reference_genome,
name='human_reference_genome',
output=human_reference_genome_file)
# Index the human reference genome with BWA, needed before we can map reads
pipeline.transform(
task_func=stages.index_ref_bwa,
name='index_ref_bwa',
input=output_from('human_reference_genome'),
filter=suffix('.fa'),
output=['.fa.amb', '.fa.ann', '.fa.pac', '.fa.sa', '.fa.bwt'])
# Align paired end reads in FASTQ to the reference producing a BAM file
(pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# We assume the sample name may consist of only alphanumeric
# characters.
filter=formatter('.+/(?P<sample>[_a-zA-Z0-9]+)_R1.fastq.gz'),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
add_inputs=add_inputs('{path[0]}/{sample[0]}_R2.fastq.gz'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}'],
# The output file name is the sample name with a .bam extension.
output='{path[0]}/{sample[0]}.bam')
.follows('index_ref_bwa'))
return pipeline
def make_pipeline_call(state):
#this part of the pipeline will take the summary results of "map" and turn them into gatk and undr_rover vcfs
pipeline = Pipeline(name='hiplexpipe')
with open("all_sample.passed.summary.txt", 'r') as inputf:
passed_files = inputf.read().split('\n')
stages = Stages(state)
safe_make_dir('variants')
safe_make_dir('variants/gatk')
safe_make_dir('variants/undr_rover')
safe_make_dir('variants/undr_rover/coverdir')
pipeline.originate(task_func=stages.passed_filter_files,
name='passed_filter_files',
output=passed_files)
# Call variants using undr_rover
pipeline.transform(
task_func=stages.apply_undr_rover,
name='apply_undr_rover',
input=output_from('passed_filter_files'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).clipped.sort.hq.bam'),
output='variants/undr_rover/{sample[0]}.vcf',
extras=['{sample[0]}'])
#### concatenate undr_rover vcfs ####
pipeline.transform(
task_func=stages.sort_vcfs,
name='sort_vcfs',
input=output_from('apply_undr_rover'),
filter=formatter('variants/undr_rover/(?P<sample>[a-zA-Z0-9_-]+).vcf'),
output='variants/undr_rover/{sample[0]}.sorted.vcf.gz')
pipeline.transform(task_func=stages.index_vcfs,
name='index_vcfs',
input=output_from('sort_vcfs'),
filter=suffix('.sorted.vcf.gz'),
output='.sorted.vcf.gz.tbi')
###### GATK VARIANT CALLING ######
# Call variants using GATK
pipeline.transform(
task_func=stages.call_haplotypecaller_gatk,
name='call_haplotypecaller_gatk',
input=output_from('passed_filter_files'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9-_]+).clipped.sort.hq.bam'),
output='variants/gatk/{sample[0]}.g.vcf')
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='twin ion')
# Get a list of paths to all the MZML files
mzml_files = state.config.get_option('mzml')
# Stages are dependent on the state
stages = Stages(state)
# The original MZML files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(
task_func=stages.original_mzml,
name='original_mzml',
output=mzml_files)
pipeline.transform(
task_func=stages.resample,
name='resample',
input=output_from('original_mzml'),
filter=suffix('.mzML'),
output='.resample.mzML')
pipeline.transform(
task_func=stages.noise_filter_sgolay,
name='noise_filter_sgolay',
input=output_from('resample'),
filter=suffix('.resample.mzML'),
output='.denoise.mzML')
pipeline.transform(
task_func=stages.baseline_filter,
name='baseline_filter',
input=output_from('noise_filter_sgolay'),
filter=suffix('.denoise.mzML'),
output='.baseline.mzML')
pipeline.transform(
task_func=stages.peak_picker_hires,
name='peak_picker_hires',
input=output_from('baseline_filter'),
filter=suffix('.baseline.mzML'),
output='.peaks.mzML')
pipeline.transform(
task_func=stages.feature_finder_centroid,
name='feature_finder_centroid',
input=output_from('peak_picker_hires'),
filter=suffix('.peaks.mzML'),
output='.featureXML')
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='test_pipeline')
# Get a list of paths to all the FASTQ files
input_files = state.config.get_option('files')
# Stages are dependent on the state
stages = Stages(state)
# The original files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(
task_func=stages.original_files,
name='original_files',
output=input_files)
pipeline.transform(
task_func=stages.stage1,
name='stage1',
input=output_from('original_files'),
filter=suffix('.0'),
output='.1')
pipeline.transform(
task_func=stages.stage2,
name='stage2',
input=output_from('stage1'),
filter=suffix('.1'),
output='.2')
pipeline.transform(
task_func=stages.stage3,
name='stage3',
input=output_from('stage2'),
filter=suffix('.2'),
output='.3')
pipeline.transform(
task_func=stages.stage4,
name='stage4',
input=output_from('stage3'),
filter=suffix('.3'),
output='.4')
pipeline.transform(
task_func=stages.stage5,
name='stage5',
input=output_from('stage4'),
filter=suffix('.4'),
output='.5')
return pipeline
def make_pipeline_call(state):
#this part of the pipeline will take the summary results of "map" and turn them into gatk and undr_rover vcfs
pipeline = Pipeline(name='genericpipe')
with open("all_sample.passed.summary.txt", 'r') as inputf:
passed_files = inputf.read().split('\n')
stages = Stages(state)
safe_make_dir('variants')
safe_make_dir('variants/gatk')
pipeline.originate(task_func=stages.passed_filter_files,
name='passed_filter_files',
output=passed_files)
###### GATK VARIANT CALLING ######
# Call variants using GATK
pipeline.transform(
task_func=stages.call_haplotypecaller_gatk,
name='call_haplotypecaller_gatk',
input=output_from('passed_filter_files'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9-_]+).sort.hq.bam'),
output='variants/gatk/{sample[0]}.g.vcf')
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='md5')
# Get a list of paths to all the input files
input_files = state.config.get_option('files')
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(
task_func=stages.original_files,
name='original_files',
output=input_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.md5_checksum,
name='md5_checksum',
input=output_from('original_files'),
filter=suffix(''),
output='.md5')
return pipeline
def make_pipeline1(
pipeline_name, # Pipelines need to have a unique name
starting_file_names):
test_pipeline = Pipeline(pipeline_name)
# We can change the starting files later using
# set_input() for transform etc.
# or set_output() for originate
# But it can be more convenient to just pass this to the function making the pipeline
#
test_pipeline.originate(task_originate, starting_file_names)\
.follows(mkdir(tempdir), mkdir(tempdir + "/testdir", tempdir + "/testdir2"))\
.posttask(touch_file(tempdir + "/testdir/whatever.txt"))
test_pipeline.transform(
task_func=task_m_to_1,
name="add_input",
# Lookup Task from function name task_originate()
# So long as this is unique in the pipeline
input=task_originate,
# requires an anchor from 3.7 onwards, see
# https://bugs.python.org/issue34982
filter=regex(r"^(.*)"),
add_inputs=add_inputs(tempdir + "/testdir/whatever.txt"),
output=r"\1.22")
test_pipeline.transform(
task_func=task_1_to_1,
name="22_to_33",
# Lookup Task from Task name
# Function name is not unique in the pipeline
input=output_from("add_input"),
filter=suffix(".22"),
output=".33")
tail_task = test_pipeline.transform(
task_func=task_1_to_1,
name="33_to_44",
# Ask Pipeline to lookup Task from Task name
input=test_pipeline["22_to_33"],
filter=suffix(".33"),
output=".44")
# Set the tail task so that users of my sub pipeline can use it as a dependency
# without knowing the details of task names
#
# Use Task() object directly without having to lookup
test_pipeline.set_tail_tasks([tail_task])
# If we try to connect a Pipeline without tail tasks defined, we have to
# specify the exact task within the Pipeline.
# Otherwise Ruffus will not know which task we mean and throw an exception
if DEBUG_do_not_define_tail_task:
test_pipeline.set_tail_tasks([])
# Set the head task so that users of my sub pipeline send input into it
# without knowing the details of task names
test_pipeline.set_head_tasks([test_pipeline[task_originate]])
return test_pipeline
def make_pipeline1(pipeline_name, # Pipelines need to have a unique name
starting_file_names):
test_pipeline = Pipeline(pipeline_name)
# We can change the starting files later using
# set_input() for transform etc.
# or set_output() for originate
# But it can be more convenient to just pass this to the function making the pipeline
#
test_pipeline.originate(task_originate, starting_file_names)\
.follows(mkdir(tempdir), mkdir(tempdir + "/testdir", tempdir + "/testdir2"))\
.posttask(touch_file(tempdir + "/testdir/whatever.txt"))
test_pipeline.transform(task_func=task_m_to_1,
name="add_input",
# Lookup Task from function name task_originate()
# So long as this is unique in the pipeline
input=task_originate,
# requires an anchor from 3.7 onwards, see
# https://bugs.python.org/issue34982
filter=regex(r"^(.*)"),
add_inputs=add_inputs(
tempdir + "/testdir/whatever.txt"),
output=r"\1.22")
test_pipeline.transform(task_func=task_1_to_1,
name="22_to_33",
# Lookup Task from Task name
# Function name is not unique in the pipeline
input=output_from("add_input"),
filter=suffix(".22"),
output=".33")
tail_task = test_pipeline.transform(task_func=task_1_to_1,
name="33_to_44",
# Ask Pipeline to lookup Task from Task name
input=test_pipeline["22_to_33"],
filter=suffix(".33"),
output=".44")
# Set the tail task so that users of my sub pipeline can use it as a dependency
# without knowing the details of task names
#
# Use Task() object directly without having to lookup
test_pipeline.set_tail_tasks([tail_task])
# If we try to connect a Pipeline without tail tasks defined, we have to
# specify the exact task within the Pipeline.
# Otherwise Ruffus will not know which task we mean and throw an exception
if DEBUG_do_not_define_tail_task:
test_pipeline.set_tail_tasks([])
# Set the head task so that users of my sub pipeline send input into it
# without knowing the details of task names
test_pipeline.set_head_tasks([test_pipeline[task_originate]])
return test_pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='fastq2bam')
# Get a list of paths to all the FASTQ files
input_files = state.config.get_option('files')
# Stages are dependent on the state
stages = Stages(state)
# The original files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_files,
name='original_files',
output=input_files)
pipeline.transform(
task_func=stages.fastq2bam,
name='fastq2bam',
input=output_from('original_files'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+)_R1.fastq.gz'),
add_inputs=add_inputs('{path[0]}/{sample[0]}_R2.fastq.gz'),
extras=['{sample[0]}'],
output='{path[0]}/out/{sample[0]}.bam')
pipeline.transform(
task_func=stages.validate_prealigned_bam,
name='validate_prealigned_bam',
input=output_from('fastq2bam'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).bam'),
output='{path[0]}/{sample[0]}.validation')
pipeline.transform(
task_func=stages.align,
name='align',
input=output_from('validate_prealigned_bam'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).validation'),
add_inputs=add_inputs('{path[0]}/{sample[0]}.bam'),
output='{path[0]}/{sample[0]}.mapped.bam')
return pipeline
def handle_input(in_, in_key, formatter_key):
formatter_inputs, simple_inputs, generator_inputs = \
aggregate_task_inputs(
ensure_list(task.get(in_, []), tuple_ok=False))
if len(generator_inputs) > 0: # generator_inputs goes to unnamed arg
task_args.extend(generator_inputs)
# simple_inputs get common general formatter
if len(simple_inputs) > 0:
formatter_inputs.append((simple_inputs, r'.+'))
# handle formatter_inputs
task_inputs = []
task_formatters = []
for in_, reg in formatter_inputs:
in_ = [
i['name'] if isinstance(i, dict) else i
for i in ensure_list(in_)
]
temp_in = []
temp_reg = reg
for i in in_:
if i in task_name_list:
temp_in.append(output_from(i))
continue
elif not os.path.isabs(i): # prepend default io dir
i = os.path.join(config.task_io_default_dir, i)
temp_reg = r'(?:[^/]*/)*' + reg
else:
pass
if re.search(r'[?*,\[\]{}]', i) is not None:
config.logger.info('{0:^27s} (glob): {1}'.format(
task_name, i))
temp_in.append(i)
else:
config.logger.info('{0:^27s} (file): {1}'.format(
task_name, i))
temp_in.append(i)
task_inputs.append(temp_in) # list of list
task_formatters.append(temp_reg) # list of regex
if len(task_inputs) > 0:
task_inputs = reduce(lambda a, b: a + b, task_inputs) # flatten
if len(task_inputs) > 0:
task_kwargs[in_key] = unwrap_if_len_one(task_inputs)
if task['pipe'] != 'merge': # require formatter for non-merge pipe
task_kwargs[formatter_key] = formatter(*task_formatters)
"{subpath[0][0][0]}",
"{subdir[0][0][0]}"]).follows("WOWWWEEE").follows(gen_task1).follows(generate_initial_files1).follows("generate_initial_files1")
test_pipeline1.merge(task_func=check_product_merged_task,
input=check_product_task,
output=tempdir + "/merged.results")
test_pipeline1.product(task_func=check_product_misspelt_capture_error_task,
input=gen_task1,
filter=formatter(".*/(?P<FILE_PART>.+).tmp1$"),
output="{path[0][0]}/{FILEPART[0][0]}.tmp2")
test_pipeline1.product(task_func=check_product_out_of_range_formatter_ref_error_task,
input=generate_initial_files1, #
filter=formatter(".*/(?P<FILE_PART>.+).tmp1$"),
output="{path[2][0]}/{basename[0][0]}.tmp2",
extras=["{FILE_PART[0][0]}"])
test_pipeline1.product(task_func=check_product_formatter_ref_index_error_task,
input=output_from("generate_initial_files1"),
filter=formatter(".*/(?P<FILE_PART>.+).tmp1$"),
output="{path[0][0][1000]}/{basename[0][0]}.tmp2",
extras=["{FILE_PART[0][0]}"])
test_pipeline1.combinations(task_func=check_combinations2_task,
input=generate_initial_files1, # gen_task1
filter=formatter(".*/(?P<FILE_PART>.+).tmp1$"),
tuple_size=2,
output="{path[0][0]}/{FILE_PART[0][0]}.{basename[1][0]}.tmp2",
extras=["{basename[0][0][0]}{basename[1][0][0]}", # extra: prefices
# extra: path for 2nd input, 1st file
"{subpath[0][0][0]}",
"{subdir[0][0][0]}"])
test_pipeline1.merge(task_func=check_combinations2_merged_task,
input=check_combinations2_task,
output=tempdir + "/merged.results")
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='methylation_pipeline')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
# Stages are dependent on the state
stages = PipelineStages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Run bismark genome preparation on the reference genome
pipeline.originate(task_func=stages.bismark_genome_prepare,
name='bismark_genome_prepare',
output='reference/Bisulfite_Genome')
# Run FASTQC on the input fastq files
pipeline.transform(
task_func=stages.fastqc,
name='fastqc',
input=output_from('original_fastqs'),
filter=formatter('(?P<path>.+)/(?P<filename>.+).fastq.gz'),
output='{path[0]}/{filename[0]}_fastqc')
# Run bismark on the input fastq files
(pipeline.transform(
task_func=stages.bismark,
name='bismark',
input=output_from('original_fastqs'),
filter=formatter(
'(?P<path>.+)/(?P<filename>.+)_R1_(?P<num>.+).fastq.gz'),
add_inputs=add_inputs('{path[0]}/{filename[0]}_R2_{num[0]}.fastq.gz'),
extras=['{path[0]}/bismark_output/'],
output=
'{path[0]}/bismark_output/{filename[0]}_R1_{num[0]}_bismark_bt2_pe.sam.gz'
)).follows('bismark_genome_prepare')
# Run bismark methylation extractor on the bismark output
pipeline.transform(
task_func=stages.bismark_methylation_extractor,
name='bismark_methylation_extractor',
input=output_from('bismark'),
filter=formatter(
'(?P<path>.+)/(?P<filename>.+)_R1_(?P<num>.+)_bismark_bt2_pe.sam.gz'
),
extras=['{path[0]}'],
output=
'{path[0]}/CpG_context_{filename[0]}_R1_{num[0]}_bismark_bt2_pe.sam.gz.txt'
)
# Run methpt on the bismark methylation extractor output
pipeline.transform(
task_func=stages.methpat,
name='methpat',
input=output_from('bismark_methylation_extractor'),
filter=formatter('(?P<path>.+)/CpG_context_(?P<filename>.+)'),
extras=['{path[0]}', '{filename[0]}'],
output='{path[0]}/CpG_context_{filename[0]}.methpat.html')
return pipeline
generate_initial_files1).follows("generate_initial_files1")
test_pipeline1.merge(task_func=test_product_merged_task,
input=test_product_task,
output=tempdir + "/merged.results")
test_pipeline1.product(task_func=test_product_misspelt_capture_error_task,
input=gen_task1,
filter=formatter(".*/(?P<FILE_PART>.+).tmp1$"),
output="{path[0][0]}/{FILEPART[0][0]}.tmp2")
test_pipeline1.product(
task_func=test_product_out_of_range_formatter_ref_error_task,
input=generate_initial_files1, #
filter=formatter(".*/(?P<FILE_PART>.+).tmp1$"),
output="{path[2][0]}/{basename[0][0]}.tmp2",
extras=["{FILE_PART[0][0]}"])
test_pipeline1.product(task_func=test_product_formatter_ref_index_error_task,
input=output_from("generate_initial_files1"),
filter=formatter(".*/(?P<FILE_PART>.+).tmp1$"),
output="{path[0][0][1000]}/{basename[0][0]}.tmp2",
extras=["{FILE_PART[0][0]}"])
test_pipeline1.combinations(
task_func=test_combinations2_task,
input=generate_initial_files1, # gen_task1
filter=formatter(".*/(?P<FILE_PART>.+).tmp1$"),
tuple_size=2,
output="{path[0][0]}/{FILE_PART[0][0]}.{basename[1][0]}.tmp2",
extras=[
"{basename[0][0][0]}{basename[1][0][0]}", # extra: prefices
"{subpath[0][0][0]}", # extra: path for 2nd input, 1st file
"{subdir[0][0][0]}"
])
test_pipeline1.merge(task_func=test_combinations2_merged_task,
def make_pipeline_process(state):
#originate process pipeline state
# Define empty pipeline
pipeline = Pipeline(name='haloplexpipe')
# Get a list of paths to all the directories to be combined for variant calling
run_directories = state.config.get_option('runs')
#grab files from each of the processed directories in "runs"
gatk_files = []
for directory in run_directories:
gatk_files.extend(glob.glob(directory + '/variants/gatk/*.g.vcf'))
stages = Stages(state)
#dummy stage to take the globbed outputs of each run that is to be processed
pipeline.originate(task_func=stages.glob_gatk,
name='glob_gatk',
output=gatk_files)
# Combine G.VCF files for all samples using GATK
pipeline.merge(task_func=stages.combine_gvcf_gatk,
name='combine_gvcf_gatk',
input=output_from('glob_gatk'),
output='processed/gatk/ALL.combined.vcf')
# Genotype G.VCF files using GATK
pipeline.transform(task_func=stages.genotype_gvcf_gatk,
name='genotype_gvcf_gatk',
input=output_from('combine_gvcf_gatk'),
filter=suffix('.combined.vcf'),
output='.raw.vcf')
# Apply GT filters to genotyped vcf
pipeline.transform(task_func=stages.genotype_filter_gatk,
name='genotype_filter_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.raw.vcf'),
output='.raw.gt-filter.vcf')
# Decompose and normalise multiallelic sites
pipeline.transform(task_func=stages.vt_decompose_normalise,
name='vt_decompose_normalise',
input=output_from('genotype_filter_gatk'),
filter=suffix('.raw.gt-filter.vcf'),
output='.raw.gt-filter.decomp.norm.vcf')
# Annotate VCF file using GATK
pipeline.transform(task_func=stages.variant_annotator_gatk,
name='variant_annotator_gatk',
input=output_from('vt_decompose_normalise'),
filter=suffix('.raw.gt-filter.decomp.norm.vcf'),
output='.raw.gt-filter.decomp.norm.annotate.vcf')
# Filter vcf
pipeline.transform(
task_func=stages.gatk_filter,
name='gatk_filter',
input=output_from('variant_annotator_gatk'),
filter=suffix('.raw.gt-filter.decomp.norm.annotate.vcf'),
output='.raw.gt-filter.decomp.norm.annotate.filter.vcf')
#Apply VEP
pipeline.transform(
task_func=stages.apply_vep,
name='apply_vep',
input=output_from('gatk_filter'),
filter=suffix('.raw.gt-filter.decomp.norm.annotate.filter.vcf'),
output='.raw.gt-filter.decomp.norm.annotate.filter.vep.vcf')
####### vardict stuff
vardict_files = []
for directory in run_directories:
vardict_files.extend(
glob.glob(directory + '/variants/vardict/*sorted.vcf.gz'))
#dummy stage to take the globbed outputs of each run that is to be processed
pipeline.originate(task_func=stages.glob_vardict,
name='glob_vardict',
output=vardict_files)
safe_make_dir('processed/vardict')
#concatenate all vardict vcfs
pipeline.merge(task_func=stages.concatenate_vcfs,
name='concatenate_vcfs',
input=output_from('glob_vardict'),
output='processed/vardict/combined.vcf.gz')
pipeline.transform(task_func=stages.vt_decompose_normalise,
name='vt_decompose_normalise_vardict',
input=output_from('concatenate_vcfs'),
filter=suffix('.vcf.gz'),
output='.decomp.norm.vcf.gz')
pipeline.transform(task_func=stages.index_vcfs,
name='index_final_vcf',
input=output_from('vt_decompose_normalise_vardict'),
filter=suffix('.decomp.norm.vcf.gz'),
output='.decomp.norm.vcf.gz.tbi')
(pipeline.transform(
task_func=stages.apply_vep,
name='apply_vep_vardict',
input=output_from('vt_decompose_normalise_vardict'),
filter=suffix('.decomp.norm.vcf.gz'),
output='.decomp.norm.vep.vcf').follows('index_final_vcf'))
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='complexo')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# We assume the sample name may consist of only alphanumeric
# characters.
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_]+)_R1.fastq.gz'),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
add_inputs=add_inputs('{path[0]}/{sample[0]}_R2.fastq.gz'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}'],
# The output file name is the sample name with a .bam extension.
output='{path[0]}/{sample[0]}.bam')
# Sort the BAM file using Picard
pipeline.transform(task_func=stages.sort_bam_picard,
name='sort_bam_picard',
input=output_from('align_bwa'),
filter=suffix('.bam'),
output='.sort.bam')
# Mark duplicates in the BAM file using Picard
pipeline.transform(
task_func=stages.mark_duplicates_picard,
name='mark_duplicates_picard',
input=output_from('sort_bam_picard'),
filter=suffix('.sort.bam'),
# XXX should make metricsup an extra output?
output=['.sort.dedup.bam', '.metricsdup'])
# Generate chromosome intervals using GATK
pipeline.transform(task_func=stages.chrom_intervals_gatk,
name='chrom_intervals_gatk',
input=output_from('mark_duplicates_picard'),
filter=suffix('.sort.dedup.bam'),
output='.chr.intervals')
# Local realignment using GATK
(pipeline.transform(
task_func=stages.local_realignment_gatk,
name='local_realignment_gatk',
input=output_from('chrom_intervals_gatk'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_]+).chr.intervals'),
add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.bam'),
output='{path[0]}/{sample[0]}.sort.dedup.realn.bam').follows(
'mark_duplicates_picard'))
# Base recalibration using GATK
pipeline.transform(task_func=stages.base_recalibration_gatk,
name='base_recalibration_gatk',
input=output_from('local_realignment_gatk'),
filter=suffix('.sort.dedup.realn.bam'),
output=['.recal_data.csv', '.count_cov.log'])
# Print reads using GATK
(pipeline.transform(
task_func=stages.print_reads_gatk,
name='print_reads_gatk',
input=output_from('base_recalibration_gatk'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_]+).recal_data.csv'),
add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.realn.bam'),
output='{path[0]}/{sample[0]}.sort.dedup.realn.recal.bam').follows(
'local_realignment_gatk'))
# Call variants using GATK
pipeline.transform(task_func=stages.call_variants_gatk,
name='call_variants_gatk',
input=output_from('print_reads_gatk'),
filter=suffix('.sort.dedup.realn.recal.bam'),
output='.raw.snps.indels.g.vcf')
# Combine G.VCF files for all samples using GATK
pipeline.merge(task_func=stages.combine_gvcf_gatk,
name='combine_gvcf_gatk',
input=output_from('call_variants_gatk'),
output='COMPLEXO.mergedgvcf.vcf')
# Genotype G.VCF files using GATK
pipeline.transform(task_func=stages.genotype_gvcf_gatk,
name='genotype_gvcf_gatk',
input=output_from('combine_gvcf_gatk'),
filter=suffix('.mergedgvcf.vcf'),
output='.genotyped.vcf')
# SNP recalibration using GATK
pipeline.transform(task_func=stages.snp_recalibrate_gatk,
name='snp_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
output=['.snp_recal', '.snp_tranches', '.snp_plots.R'])
# INDEL recalibration using GATK
pipeline.transform(
task_func=stages.indel_recalibrate_gatk,
name='indel_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
output=['.indel_recal', '.indel_tranches', '.indel_plots.R'])
# Apply SNP recalibration using GATK
(pipeline.transform(
task_func=stages.apply_snp_recalibrate_gatk,
name='apply_snp_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
add_inputs=add_inputs(['COMPLEXO.snp_recal', 'COMPLEXO.snp_tranches']),
output='.recal_SNP.vcf').follows('snp_recalibrate_gatk'))
# Apply INDEL recalibration using GATK
(pipeline.transform(
task_func=stages.apply_indel_recalibrate_gatk,
name='apply_indel_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
add_inputs=add_inputs(
['COMPLEXO.indel_recal', 'COMPLEXO.indel_tranches']),
output='.recal_INDEL.vcf').follows('indel_recalibrate_gatk'))
# Combine variants using GATK
(pipeline.transform(
task_func=stages.combine_variants_gatk,
name='combine_variants_gatk',
input=output_from('apply_snp_recalibrate_gatk'),
filter=suffix('.recal_SNP.vcf'),
add_inputs=add_inputs(['COMPLEXO.recal_INDEL.vcf']),
output='.combined.vcf').follows('apply_indel_recalibrate_gatk'))
# Select variants using GATK
pipeline.transform(task_func=stages.select_variants_gatk,
name='select_variants_gatk',
input=output_from('combine_variants_gatk'),
filter=suffix('.combined.vcf'),
output='.selected.vcf')
return pipeline
def main():
# prepare the ruffus pipeline
main_pipeline = ruffus.Pipeline.pipelines["main"]
# catch jgi logon and password from cli
parser = ruffus.cmdline.get_argparse(description='UV-B analysis pipeline.')
parser.add_argument('--email', '-e',
help='Logon email address for JGI',
type=str,
dest='jgi_logon')
parser.add_argument('--password', '-p',
help='JGI password',
type=str,
dest='jgi_password')
options = parser.parse_args()
jgi_logon = options.jgi_logon
jgi_password = options.jgi_password
# need a dictionary of species to genome URL and species to gff.
# supply this in a text file
fasta_urls = {}
annotation_urls = {}
with open('data/genomeUrls.txt') as tsv:
genome_urls = csv.reader(tsv, delimiter='\t')
next(genome_urls, None)
for row in genome_urls:
fasta_urls[row[0]] = row[1]
annotation_urls[row[0]] = row[2]
# iterate over fasta_urls keys to run jobs
for species in fasta_urls.keys():
# call download script
main_pipeline.originate(
name=species + "_genome",
task_func=download_genome,
output="data/genome/" + species + "/METADATA.csv",
extras=[species, fasta_urls[species], annotation_urls[species],
jgi_logon, jgi_password])
# generate a star genome for each species
main_pipeline.transform(
name=species + "_index",
task_func=generate_index,
input=ruffus.output_from(species + "_genome"),
filter=ruffus.regex(r"data/genome/(.*)/METADATA.csv"),
output=r"output/\1/star-index/METADATA.csv",
extras=[r"\1"])
# define the reads
main_pipeline.originate(name=species + "_reads",
task_func=define_reads,
output="ruffus/" + species + "_reads",
extras=[species])
# first mapping step
main_pipeline.collate(
name=species + "_mapped_reads",
task_func=star,
input=[[ruffus.output_from(species + "_reads"),
ruffus.output_from(species + "_index")]],
filter=ruffus.formatter(),
output=["output/{subdir[1][1]}/star/METADATA.csv"],
extras=["{subdir[1][1]}"])
# FOR LOOP ENDS
# parse the mapping stats
mapping_stats = main_pipeline.merge(
task_func=parse_star_stats_R,
input=ruffus.output_from(
list(species + "_mapped_reads" for species in fasta_urls.keys())),
output="output/mapping_stats/SessionInfo.txt")
# generate plots for mapping
mapping_plots = main_pipeline.transform(
task_func=plot_reads_in_genes_R,
input=mapping_stats,
filter=ruffus.formatter(),
output="{subpath[0][0]}/Figure S1.pdf")
# use generator in the input field to collate the previous results
deseq_results = main_pipeline.transform(
task_func=deseq2_R,
input=ruffus.output_from(
list(species + "_mapped_reads"
for species in fasta_urls.keys())),
filter=ruffus.formatter(),
output=[r"output/{subdir[0][1]}/deseq2/SessionInfo.txt"],
extras=[r"{subdir[0][1]}"])
# combine the deseq results
de_lists = main_pipeline.merge(
task_func=list_de_genes_R,
input=deseq_results,
output="output/merged/deseq2/SessionInfo.de_genes.txt")
# run clustering
mfuzz_results = main_pipeline.transform(
task_func=mfuzz_R,
input=deseq_results,
filter=ruffus.formatter(),
output='output/{subdir[0][1]}/mfuzz/SessionInfo.mfuzz.txt',
extras=['{subdir[0][1]}'])
# combine mfuzz_results
mfuzz_plot = main_pipeline.merge(
task_func=combine_mfuzz_results_R,
input=mfuzz_results,
output='output/merged/mfuzz/SessionInfo.mfuzz.txt')
# compare flavonoid synthesis genes
flavonoid_genes = main_pipeline.transform(
task_func=compare_saito_genes_R,
input=de_lists,
filter=ruffus.formatter(),
output='{path[0]}/SessionInfo.flavonoid_synthesis.txt')
# run the pipeline
ruffus.cmdline.run(options, multithread=8)
# print the flowchart
ruffus.pipeline_printout_graph("ruffus/flowchart.pdf", "pdf",
pipeline_name="UV-B analysis pipeline")
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='thepipeline')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(
task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# We assume the sample name may consist of only alphanumeric
# characters.
# filter=formatter('(?P<path>.+)/(?P<readid>[a-zA-Z0-9-\.]+)_(?P<lib>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9]+)_1.fastq.gz'),
# 1_HFYLVCCXX:2:TCCGCGAA_2_GE0343_1.fastq.gz
# 1_HCJWFBCXX:GGACTCCT_L001_9071584415739518822-AGRF-023_R2.fastq.gz
filter=formatter(
'.+/(?P<readid>[a-zA-Z0-9-]+)_(?P<lib>[a-zA-Z0-9-:]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9-]+)_R1.fastq.gz'),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
# e.g. C2WPF.5_Solexa-201237_5_X4311_1.fastq.gz
add_inputs=add_inputs(
'{path[0]}/{readid[0]}_{lib[0]}_{lane[0]}_{sample[0]}_R2.fastq.gz'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{readid[0]}', '{lib[0]}', '{lane[0]}', '{sample[0]}'],
# extras=['{sample[0]}'],
# The output file name is the sample name with a .bam extension.
output='alignments/{sample[0]}/{readid[0]}_{lib[0]}_{lane[0]}_{sample[0]}.bam')
# Sort the BAM file using Picard
pipeline.transform(
task_func=stages.sort_bam_picard,
name='sort_bam_picard',
input=output_from('align_bwa'),
filter=suffix('.bam'),
output='.sort.bam')
# Mark duplicates in the BAM file using Picard
pipeline.transform(
task_func=stages.mark_duplicates_picard,
name='mark_duplicates_picard',
input=output_from('sort_bam_picard'),
filter=suffix('.sort.bam'),
# XXX should make metricsup an extra output?
output=['.sort.dedup.bam', '.metricsdup'])
# Local realignment using GATK
# Generate RealignerTargetCreator using GATK
pipeline.transform(
task_func=stages.realigner_target_creator,
name='realigner_target_creator',
input=output_from('mark_duplicates_picard'),
filter=suffix('.sort.dedup.bam'),
output='.intervals')
# Local realignment using GATK
(pipeline.transform(
task_func=stages.local_realignment_gatk,
name='local_realignment_gatk',
input=output_from('realigner_target_creator'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).chr.intervals'),
filter=formatter(
'.+/(?P<readid>[a-zA-Z0-9-]+)_(?P<lib>[a-zA-Z0-9-:]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9-]+).intervals'),
# add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.bam'),
add_inputs=add_inputs(
'alignments/{sample[0]}/{readid[0]}_{lib[0]}_{lane[0]}_{sample[0]}.sort.dedup.bam'),
output='alignments/{sample[0]}/{readid[0]}_{lib[0]}_{lane[0]}_{sample[0]}.sort.dedup.realn.bam')
.follows('mark_duplicates_picard'))
# Base recalibration using GATK
pipeline.transform(
task_func=stages.base_recalibration_gatk,
name='base_recalibration_gatk',
input=output_from('local_realignment_gatk'),
filter=suffix('.sort.dedup.realn.bam'),
output=['.recal_data.csv', '.count_cov.log'])
# Print reads using GATK
(pipeline.transform(
task_func=stages.print_reads_gatk,
name='print_reads_gatk',
input=output_from('base_recalibration_gatk'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).recal_data.csv'),
filter=formatter(
# '.+/(?P<readid>[a-zA-Z0-9-\.]+)_(?P<lib>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9]+).recal_data.csv'),
'.+/(?P<readid>[a-zA-Z0-9-]+)_(?P<lib>[a-zA-Z0-9-:]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9-]+).recal_data.csv'),
# '.+/(?P<readid>[a-zA-Z0-9-]+)_(?P<lib>[a-zA-Z0-9-:]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9-]+).recal_data.csv'),
# add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.realn.bam'),
add_inputs=add_inputs(
'alignments/{sample[0]}/{readid[0]}_{lib[0]}_{lane[0]}_{sample[0]}.sort.dedup.realn.bam'),
# output='{path[0]}/{sample[0]}.sort.dedup.realn.recal.bam')
output='alignments/{sample[0]}/{readid[0]}_{lib[0]}_{lane[0]}_{sample[0]}.sort.dedup.realn.recal.bam')
.follows('local_realignment_gatk'))
# Merge lane bams to sample bams
pipeline.collate(
task_func=stages.merge_sample_bams,
name='merge_sample_bams',
filter=formatter(
# '.+/(?P<readid>[a-zA-Z0-9-\.]+)_(?P<lib>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9]+).sort.dedup.realn.recal.bam'),
'.+/(?P<readid>[a-zA-Z0-9-]+)_(?P<lib>[a-zA-Z0-9-:]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9-]+).sort.dedup.realn.recal.bam'),
# inputs=add_inputs('alignments/{sample[0]}/{readid[0]}_{lib[0]}_{lane[0]}_{sample[0]}.sort.dedup.realn.bam'),
input=output_from('print_reads_gatk'),
output='alignments/{sample[0]}/{sample[0]}.merged.bam')
# Mark duplicates in the BAM file using Picard
pipeline.transform(
task_func=stages.mark_duplicates_picard,
name='mark_duplicates_picard2',
input=output_from('merge_sample_bams'),
# filter=formatter(
# '.+/(?P<readid>[a-zA-Z0-9-\.]+)_(?P<lib>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9]+).merged.bam'),
filter=suffix('.merged.bam'),
# XXX should make metricsup an extra output?
output=['.merged.dedup.bam', '.metricsdup'])
# Local realignment2 using GATK
# Generate RealignerTargetCreator using GATK
pipeline.transform(
task_func=stages.realigner_target_creator,
name='realigner_target_creator2',
input=output_from('mark_duplicates_picard2'),
filter=suffix('.dedup.bam'),
output='.intervals')
# Local realignment using GATK
(pipeline.transform(
task_func=stages.local_realignment_gatk,
name='local_realignment_gatk2',
input=output_from('realigner_target_creator2'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9-]+).merged.intervals'),
# filter=formatter(
# '.+/(?P<readid>[a-zA-Z0-9-\.]+)_(?P<lib>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_(?P<sample>[a-zA-Z0-9]+).intervals'),
# add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.bam'),
add_inputs=add_inputs(
'alignments/{sample[0]}/{sample[0]}.merged.dedup.bam'),
output='alignments/{sample[0]}/{sample[0]}.merged.dedup.realn.bam')
.follows('mark_duplicates_picard2'))
# Call variants using GATK
pipeline.transform(
task_func=stages.call_haplotypecaller_gatk,
name='call_haplotypecaller_gatk',
input=output_from('local_realignment_gatk2'),
# filter=suffix('.merged.dedup.realn.bam'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9-]+).merged.dedup.realn.bam'),
output='variants/{sample[0]}.g.vcf')
# Combine G.VCF files for all samples using GATK
pipeline.merge(
task_func=stages.combine_gvcf_gatk,
name='combine_gvcf_gatk',
input=output_from('call_haplotypecaller_gatk'),
output='variants/ALL.combined.vcf')
# Genotype G.VCF files using GATK
pipeline.transform(
task_func=stages.genotype_gvcf_gatk,
name='genotype_gvcf_gatk',
input=output_from('combine_gvcf_gatk'),
filter=suffix('.combined.vcf'),
output='.raw.vcf')
# SNP recalibration using GATK
pipeline.transform(
task_func=stages.snp_recalibrate_gatk,
name='snp_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.raw.vcf'),
output=['.snp_recal', '.snp_tranches', '.snp_plots.R'])
# INDEL recalibration using GATK
pipeline.transform(
task_func=stages.indel_recalibrate_gatk,
name='indel_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.raw.vcf'),
output=['.indel_recal', '.indel_tranches', '.indel_plots.R'])
# Apply SNP recalibration using GATK
(pipeline.transform(
task_func=stages.apply_snp_recalibrate_gatk,
name='apply_snp_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.raw.vcf'),
add_inputs=add_inputs(['ALL.snp_recal', 'ALL.snp_tranches']),
output='.recal_SNP.vcf')
.follows('snp_recalibrate_gatk'))
# Apply INDEL recalibration using GATK
(pipeline.transform(
task_func=stages.apply_indel_recalibrate_gatk,
name='apply_indel_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.raw.vcf'),
add_inputs=add_inputs(
['ALL.indel_recal', 'ALL.indel_tranches']),
output='.recal_INDEL.vcf')
.follows('indel_recalibrate_gatk'))
# Combine variants using GATK
(pipeline.transform(
task_func=stages.combine_variants_gatk,
name='combine_variants_gatk',
input=output_from('apply_snp_recalibrate_gatk'),
filter=suffix('.recal_SNP.vcf'),
add_inputs=add_inputs(['ALL.recal_INDEL.vcf']),
# output='.combined.vcf')
output='ALL.raw.vqsr.vcf')
.follows('apply_indel_recalibrate_gatk'))
#
# # Select variants using GATK
# pipeline.transform(
# task_func=stages.select_variants_gatk,
# name='select_variants_gatk',
# input=output_from('combine_variants_gatk'),
# filter=suffix('.combined.vcf'),
# output='.selected.vcf')
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name="radpipe")
# Stages are dependent on the state
stages = PipelineStages(state)
# Get a list of library objects.
libraries = parse_libraries(
libraries=state.config.get_options("libraries"))
# Get a list of input files
input_files = [l.files for l in libraries]
# input_files = [item for sublist in input_files for item in sublist]
state.logger.info("Input files: " + str(input_files))
# Get a list of all samples for each library
samples_dict = OrderedDict()
for l in libraries:
samples_dict[l.name] = l.samples
state.logger.debug("Samples: " + str(samples_dict))
# Make sure that there are no duplicate samples
sample_list = [
item for sublist in samples_dict.values() for item in sublist
]
sample_counts = Counter(sample_list)
for sample in sample_counts:
if sample_counts[sample] > 1:
print("Sample {} appears {} times in the barcodes files. "
"Sample names must be unique".format(sample,
sample_counts[sample]))
sys.exit(radpipe.error_codes.INVALID_INPUT_FILE)
# Define output directories
output_dir = get_output_paths(state)
state.logger.debug(output_dir)
# Allow multiple comma-separated tasks
if len(state.options.target_tasks) == 1:
state.options.target_tasks = state.options.target_tasks[0].split(",")
if len(state.options.forced_tasks) == 1:
state.options.forced_tasks = state.options.forced_tasks[0].split(",")
state.logger.debug("Target tasks: " + str(state.options.target_tasks))
state.logger.debug("Forced tasks: " + str(state.options.forced_tasks))
# Check if alignment_method is valid
alignment_method = state.config.get_options(
"alignment_method").strip().lower()
if alignment_method not in ["bwa mem", "bowtie"]:
print("Error: Invalid alignment_method in config file. " \
"Valid options are ['bwa mem', 'bowtie'].")
sys.exit(radpipe.error_codes.INVALID_ARGUMENT)
if alignment_method == "bwa mem":
align_task_name = "bwa_mem"
index_task_name = "bwa_index"
else:
align_task_name = "bowtie"
index_task_name = "bowtie_index"
# TODO: Refactor this
# If 'alignment' is in target_tasks or forced_tasks, specify which
# type of alignment job
if "alignment" in state.options.target_tasks:
index = state.options.target_tasks.index("alignment")
state.options.target_tasks[index] = align_task_name
if "alignment" in state.options.forced_tasks:
index = state.options.forced_tasks.index("alignment")
state.options.forced_tasks[index] = align_task_name
# If 'build_index' is in target_tasks or forced_tasks, specify which
# type of index job
if "build_index" in state.options.target_tasks:
index = state.options.target_tasks.index("build_index")
state.options.target_tasks[index] = index_task_name
if "build_index" in state.options.forced_tasks:
index = state.options.forced_tasks.index("build_index")
state.options.forced_tasks[index] = index_task_name
state.logger.debug(state)
# Whether to include filter_bam stage or not
filter_bams = False
try:
samtools_view_options = state.config.get_options(
"samtools_view_options")
if samtools_view_options:
filter_bams = True
except:
pass
state.logger.info("Filter bams: {}".format(filter_bams))
# Population map filenames
popmap_file = "{output_dir}/{name}_popmap.txt".format(
output_dir=output_dir["populations"],
name=state.config.get_options("analysis_id"))
try:
config_popmap_file = state.config.get_options("popmap_file")
if config_popmap_file:
state.logger.info(
"Using popmap file: {}".format(config_popmap_file))
else:
raise (Exception)
except Exception:
config_popmap_file = None
state.logger.info("Creating new popmap file: {}".format(popmap_file))
# Population r values
populations_r = state.config.get_options("populations_r")
assert (isinstance(populations_r, list))
# Dummy stages. These do nothing except provide a node at the beginning
# for the pipeline graph, giving the pipeline an obvious starting point.
pipeline.originate(task_func=stages.do_nothing,
name="original_fastqs",
output=input_files)
pipeline.originate(task_func=stages.do_nothing,
name="reference_genome",
output=state.config.get_options("reference_genome"))
# Create a copy of the population map file needed for stacks, or create
# one denovo using the sample list.
pipeline.originate(task_func=stages.create_popmap_file,
name="create_popmap_file",
output=[popmap_file],
extras=[config_popmap_file, sample_list])
# Create index for reference genome based on alignment method.
if alignment_method == "bwa mem":
pipeline.transform(
task_func=stages.bwa_index,
name="bwa_index",
input=output_from("reference_genome"),
filter=formatter(".+/(?P<ref>[^/]+).(fa|fasta)"),
output=path_list_join(output_dir["reference"],
["reference.fa.bwt", "reference.fa.sa"]),
extras=[output_dir["reference"]])
if alignment_method == "bowtie":
pipeline.transform(task_func=stages.bowtie_index,
name="bowtie_index",
input=output_from("reference_genome"),
filter=formatter(".+/(?P<ref>[^/]+).(fa|fasta)"),
output=path_list_join(
output_dir["reference"],
["reference.1.ebwt", "reference.rev.1.ebwt"]),
extras=[output_dir["reference"]])
# FastQC
pipeline.transform(
task_func=stages.fastqc,
name="fastqc",
input=output_from("original_fastqs"),
filter=formatter(".+/(?P<lib>[^/]+)/(?P<fn>[^/]+).(fastq|fq).gz"),
output="%s/{lib[0]}/{fn[0]}_fastqc.zip" % output_dir["fastqc"],
extras=[output_dir["fastqc"], "{lib[0]}"])
# MultiQC: FastQC
pipeline.merge(task_func=stages.multiqc_fastqc,
name="multiqc_fastqc",
input=output_from("fastqc"),
output="%s/multiqc_fastqc_report.html" % output_dir["qc"],
extras=[output_dir["qc"], output_dir["fastqc"]])
# Stacks: Process RAD-Tags
pipeline.transform(task_func=stages.process_radtags,
name="process_radtags",
input=output_from("original_fastqs"),
filter=formatter(".+/(?P<lib>[^/]+)/[^/]+"),
output="%s/{lib[0]}/{lib[0]}.success" %
output_dir["process_radtags"],
extras=[
output_dir["process_radtags"], "{lib[0]}",
state.config.get_options("renz_1"),
state.config.get_options("renz_2"),
state.config.get_options("process_radtags_options")
])
# Create a list for alignment with the input fastq files from process_radtags
process_radtags_outputs = []
for l in libraries:
for s in l.samples:
base = "{dir}/{lib}/{sample}".format(
dir=output_dir["process_radtags"], lib=l.lib_id, sample=s)
process_radtags_outputs.append(
[base + ".1.fq.gz", base + ".2.fq.gz"])
# print(process_radtags_outputs)
# Alignment
if align_task_name == "bwa_mem":
(pipeline.transform(
task_func=stages.bwa_align,
name=align_task_name,
input=process_radtags_outputs,
filter=formatter(".+/(?P<sm>[^/]+).1.fq.gz"),
output="%s/{sm[0]}.bwa.bam" % output_dir["alignments"],
extras=[
os.path.join(output_dir["reference"], "reference.fa"),
"{path[0]}", output_dir["alignments"], "{sm[0]}",
state.config.get_options("alignment_options")
])).follows("bwa_index").follows("process_radtags")
if align_task_name == "bowtie":
(pipeline.transform(
task_func=stages.bowtie_align,
name=align_task_name,
input=process_radtags_outputs,
filter=formatter(".+/(?P<sm>[^/]+).1.fq.gz"),
output="%s/{sm[0]}.bowtie.bam" % output_dir["alignments"],
extras=[
os.path.join(output_dir["reference"], "reference"),
"{path[0]}", output_dir["alignments"], "{sm[0]}",
state.config.get_options("alignment_options")
])).follows("bowtie_index").follows("process_radtags")
# Sort BAM and index
pipeline.transform(task_func=stages.sort_bam,
name="sort_bam",
input=output_from(align_task_name),
filter=suffix(".bam"),
output=".sorted.bam")
if filter_bams:
final_bam_task_name = "filter_bam"
pipeline.transform(
task_func=stages.filter_bam,
name="filter_bam",
input=output_from("sort_bam"),
filter=suffix(".sorted.bam"),
output=".sorted.filtered.bam",
extras=[state.config.get_options("samtools_view_options")])
else:
final_bam_task_name = "sort_bam"
# Samtools flagstat
pipeline.transform(task_func=stages.flagstat,
name="flagstat",
input=output_from(final_bam_task_name),
filter=suffix(".bam"),
output=".flagstat.txt",
output_dir=output_dir["flagstat"])
# MultiQC: flagstat
pipeline.merge(task_func=stages.multiqc_flagstat,
name="multiqc_flagstat",
input=output_from("flagstat"),
output="%s/multiqc_flagstat_report.html" % output_dir["qc"],
extras=[output_dir["qc"], output_dir["flagstat"]])
# Stacks: gstacks
pipeline.merge(task_func=stages.gstacks,
name="gstacks",
input=output_from(final_bam_task_name),
output="%s/catalog.fa.gz" % output_dir["gstacks"],
extras=[
output_dir["alignments"], output_dir["gstacks"],
align_task_name, final_bam_task_name, sample_list,
state.config.get_options("gstacks_options")
])
# Define outputs from each run of populations
populations_outputs = []
for r in populations_r:
dir_name = "{pop_dir}/{analysis_name}_r{r}".format(
pop_dir=output_dir["populations"],
analysis_name=state.config.get_options("analysis_id"),
r=r)
populations_outputs.append(
os.path.join(dir_name, "populations.snps.vcf"))
# print(populations_outputs)
# Stacks: populations
pipeline.originate(task_func=stages.populations,
name="popluations",
output=populations_outputs,
extras=[
output_dir["gstacks"], output_dir["populations"],
popmap_file,
state.config.get_options("populations_options")
]).follows("gstacks").follows("create_popmap_file")
return pipeline
def make_pipeline_process(state):
# Define empty pipeline
pipeline = Pipeline(name='hiplexpipe')
# Get a list of paths to all the directories to be combined for variant calling
run_directories = state.config.get_option('runs')
#grab files from each of the processed directories in "runs"
gatk_files = []
undr_rover_files = []
for directory in run_directories:
gatk_files.extend(glob.glob(directory + '/variants/gatk/*.g.vcf'))
undr_rover_files.extend(
glob.glob(directory + '/variants/undr_rover/*sorted.vcf.gz'))
# Stages are dependent on the state
stages = Stages(state)
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.glob_gatk,
name='glob_gatk',
output=gatk_files)
#Dummy stage to grab the undr rover files
pipeline.originate(task_func=stages.glob_undr_rover,
name='glob_undr_rover',
output=undr_rover_files)
safe_make_dir('variants')
safe_make_dir('variants/gatk')
safe_make_dir('variants/undr_rover')
pipeline.merge(task_func=stages.concatenate_vcfs,
name='concatenate_vcfs',
input=output_from('glob_undr_rover'),
output='variants/undr_rover/combined_undr_rover.vcf.gz')
pipeline.transform(task_func=stages.index_final_vcf,
name='index_final_vcf',
input=output_from('concatenate_vcfs'),
filter=suffix('.vcf.gz'),
output='.vcf.gz.tbi')
# Combine G.VCF files for all samples using GATK
pipeline.merge(task_func=stages.combine_gvcf_gatk,
name='combine_gvcf_gatk',
input=output_from('glob_gatk'),
output='ALL.combined.vcf')
# Genotype G.VCF files using GATK
pipeline.transform(task_func=stages.genotype_gvcf_gatk,
name='genotype_gvcf_gatk',
input=output_from('combine_gvcf_gatk'),
filter=suffix('.combined.vcf'),
output='.raw.vcf')
# Apply GT filters to genotyped vcf
pipeline.transform(task_func=stages.genotype_filter_gatk,
name='genotype_filter_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.raw.vcf'),
output='.raw.gt-filter.vcf')
# Decompose and normalise multiallelic sites
pipeline.transform(task_func=stages.vt_decompose_normalise,
name='vt_decompose_normalise',
input=output_from('genotype_filter_gatk'),
filter=suffix('.raw.gt-filter.vcf'),
output='.raw.gt-filter.decomp.norm.vcf')
# Annotate VCF file using GATK
pipeline.transform(task_func=stages.variant_annotator_gatk,
name='variant_annotator_gatk',
input=output_from('vt_decompose_normalise'),
filter=suffix('.raw.gt-filter.decomp.norm.vcf'),
output='.raw.gt-filter.decomp.norm.annotate.vcf')
# Filter vcf
pipeline.transform(
task_func=stages.gatk_filter,
name='gatk_filter',
input=output_from('variant_annotator_gatk'),
filter=suffix('.raw.gt-filter.decomp.norm.annotate.vcf'),
output='.raw.gt-filter.decomp.norm.annotate.filter.vcf')
#Apply VEP
(pipeline.transform(
task_func=stages.apply_vep,
name='apply_vep',
input=output_from('gatk_filter'),
filter=suffix('.raw.gt-filter.decomp.norm.annotate.filter.vcf'),
add_inputs=add_inputs(
['variants/undr_rover/combined_undr_rover.vcf.gz']),
output='.raw.gt-filter.decomp.norm.annotate.filter.vep.vcf').follows(
'index_final_vcf'))
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='vcf_annotation')
# Get a list of paths to all the FASTQ files
vcf_files = state.config.get_option('vcfs')
# Stages are dependent on the state
stages = Stages(state)
# The original VCF files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(
task_func=stages.original_vcf,
name='original_vcf',
output=vcf_file)
# Decompose VCF using Vt
pipeline.transform(
task_func=stages.decompose_vcf,
name='decompose_vcf',
input=output_from('original_vcf'),
# This will be the first input to the stage.
# We assume the sample name may consist of only alphanumeric
# characters.
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).vcf'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the VCF file name (e.g. study/family name.
# This is needed within the stage for finding out sample specific
# configuration options
extras=['{sample[0]}'],
# The output file name is the sample name with a .bam extension.
output='{path[0]}/{sample[0]}.decompose.normalize.vcf')
# FILTER COMMON VARIANTS
# ADD FILTER COMMON VARIANTS USING VEP
# Annotate using VEP
pipeline.transform(
task_func=stages.annotate_vep,
name='annotate_vep',
input=output_from('decompose_vcf'),
filter=suffix('.vcf'),
output='.vep.vcf')
# Annotate using SnpEff
pipeline.transform(
task_func=stages.annotate_snpeff,
name='annotate_snpeff',
input=output_from('annotate_vep'),
filter=suffix('.vcf'),
output='.snpeff.vcf')
# Mark duplicates in the BAM file using Picard
pipeline.transform(
task_func=stages.mark_duplicates_picard,
name='mark_duplicates_picard',
input=output_from('sort_bam_picard'),
filter=suffix('.sort.bam'),
# XXX should make metricsup an extra output?
output=['.sort.dedup.bam', '.metricsdup'])
# Generate chromosome intervals using GATK
pipeline.transform(
task_func=stages.chrom_intervals_gatk,
name='chrom_intervals_gatk',
input=output_from('mark_duplicates_picard'),
filter=suffix('.sort.dedup.bam'),
output='.chr.intervals')
# Local realignment using GATK
(pipeline.transform(
task_func=stages.local_realignment_gatk,
name='local_realignment_gatk',
input=output_from('chrom_intervals_gatk'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).chr.intervals'),
add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.bam'),
output='{path[0]}/{sample[0]}.sort.dedup.realn.bam')
.follows('mark_duplicates_picard'))
# Base recalibration using GATK
pipeline.transform(
task_func=stages.base_recalibration_gatk,
name='base_recalibration_gatk',
input=output_from('local_realignment_gatk'),
filter=suffix('.sort.dedup.realn.bam'),
output=['.recal_data.csv', '.count_cov.log'])
# Print reads using GATK
(pipeline.transform(
task_func=stages.print_reads_gatk,
name='print_reads_gatk',
input=output_from('base_recalibration_gatk'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).recal_data.csv'),
add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.realn.bam'),
output='{path[0]}/{sample[0]}.sort.dedup.realn.recal.bam')
.follows('local_realignment_gatk'))
# Call variants using GATK
pipeline.transform(
task_func=stages.call_variants_gatk,
name='call_variants_gatk',
input=output_from('print_reads_gatk'),
filter=suffix('.sort.dedup.realn.recal.bam'),
output='.raw.snps.indels.g.vcf')
# Combine G.VCF files for all samples using GATK
pipeline.merge(
task_func=stages.combine_gvcf_gatk,
name='combine_gvcf_gatk',
input=output_from('call_variants_gatk'),
output='PCExomes.mergegvcf.vcf')
# Genotype G.VCF files using GATK
pipeline.transform(
task_func=stages.genotype_gvcf_gatk,
name='genotype_gvcf_gatk',
input=output_from('combine_gvcf_gatk'),
filter=suffix('.mergegvcf.vcf'),
output='.genotyped.vcf')
# SNP recalibration using GATK
pipeline.transform(
task_func=stages.snp_recalibrate_gatk,
name='snp_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
output=['.snp_recal', '.snp_tranches', '.snp_plots.R'])
# INDEL recalibration using GATK
pipeline.transform(
task_func=stages.indel_recalibrate_gatk,
name='indel_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
output=['.indel_recal', '.indel_tranches', '.indel_plots.R'])
# Apply SNP recalibration using GATK
(pipeline.transform(
task_func=stages.apply_snp_recalibrate_gatk,
name='apply_snp_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
add_inputs=add_inputs(['PCExomes.snp_recal', 'PCExomes.snp_tranches']),
output='.recal_SNP.vcf')
.follows('snp_recalibrate_gatk'))
# Apply INDEL recalibration using GATK
(pipeline.transform(
task_func=stages.apply_indel_recalibrate_gatk,
name='apply_indel_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
add_inputs=add_inputs(
['PCExomes.indel_recal', 'PCExomes.indel_tranches']),
output='.recal_INDEL.vcf')
.follows('indel_recalibrate_gatk'))
# Combine variants using GATK
(pipeline.transform(
task_func=stages.combine_variants_gatk,
name='combine_variants_gatk',
input=output_from('apply_snp_recalibrate_gatk'),
filter=suffix('.recal_SNP.vcf'),
add_inputs=add_inputs(['PCExomes.recal_INDEL.vcf']),
output='.combined.vcf')
.follows('apply_indel_recalibrate_gatk'))
# Select variants using GATK
pipeline.transform(
task_func=stages.select_variants_gatk,
name='select_variants_gatk',
input=output_from('combine_variants_gatk'),
filter=suffix('.combined.vcf'),
output='.selected.vcf')
return pipeline
def make_pipeline_map(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='hiplexpipe')
# Stages are dependent on the state
stages = Stages(state)
safe_make_dir('alignments')
safe_make_dir('metrics')
safe_make_dir('metrics/amplicon')
safe_make_dir('metrics/summary')
# The original FASTQ files
fastq_files = glob.glob('fastqs/*')
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9_-]+)_R1_(?P<lib>[a-zA-Z0-9-:]+).fastq.gz'
),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
add_inputs=add_inputs('{path[0]}/{sample[0]}_R2_{lib[0]}.fastq.gz'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}', '{lib[0]}'],
# The output file name is the sample name with a .bam extension.
output='alignments/{sample[0]}.clipped.sort.hq.bam')
# generate mapping metrics.
pipeline.transform(
task_func=stages.generate_amplicon_metrics,
name='generate_amplicon_metrics',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).clipped.sort.hq.bam'),
output='metrics/amplicon/{sample[0]}.amplicon-metrics.txt',
extras=['{sample[0]}'])
pipeline.transform(
task_func=stages.intersect_bed,
name='intersect_bed',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).clipped.sort.hq.bam'),
output='metrics/summary/{sample[0]}.intersectbed.bam')
pipeline.transform(task_func=stages.coverage_bed,
name='coverage_bed',
input=output_from('intersect_bed'),
filter=suffix('.intersectbed.bam'),
output='.bedtools_hist_all.txt')
pipeline.transform(
task_func=stages.genome_reads,
name='genome_reads',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).clipped.sort.hq.bam'),
output='metrics/summary/{sample[0]}.mapped_to_genome.txt')
pipeline.transform(task_func=stages.target_reads,
name='target_reads',
input=output_from('intersect_bed'),
filter=suffix('.intersectbed.bam'),
output='.mapped_to_target.txt')
pipeline.transform(
task_func=stages.total_reads,
name='total_reads',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).clipped.sort.hq.bam'),
output='metrics/summary/{sample[0]}.total_raw_reads.txt')
pipeline.collate(
task_func=stages.generate_stats,
name='generate_stats',
input=output_from('coverage_bed', 'genome_reads', 'target_reads',
'total_reads'),
#filter=regex(r'.+/(.+BS\d{4,6}.+)\..+\.txt'),
filter=regex(
r'.+/(.+)\.(bedtools_hist_all|mapped_to_genome|mapped_to_target|total_raw_reads)\.txt'
),
output=r'metrics/summary/all_sample.summary.\1.txt',
extras=[r'\1', 'all_sample.summary.txt'])
summary_file = 'all_sample.summary.txt'
(pipeline.originate(task_func=stages.grab_summary_file,
name='grab_summary_file',
output=summary_file).follows('generate_stats'))
pipeline.transform(task_func=stages.filter_stats,
name='filter_stats',
input=output_from('grab_summary_file'),
filter=suffix('.summary.txt'),
output='.passed.summary.txt',
extras=['all_sample.failed.summary.txt'])
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='complexo')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(
task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# We assume the sample name may consist of only alphanumeric
# characters.
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+)_R1.fastq.gz'),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
add_inputs=add_inputs('{path[0]}/{sample[0]}_R2.fastq.gz'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}'],
# The output file name is the sample name with a .bam extension.
output='{path[0]}/{sample[0]}.bam')
# Sort the BAM file using Picard
pipeline.transform(
task_func=stages.sort_bam_picard,
name='sort_bam_picard',
input=output_from('align_bwa'),
filter=suffix('.bam'),
output='.sort.bam')
# Mark duplicates in the BAM file using Picard
pipeline.transform(
task_func=stages.mark_duplicates_picard,
name='mark_duplicates_picard',
input=output_from('sort_bam_picard'),
filter=suffix('.sort.bam'),
# XXX should make metricsup an extra output?
output=['.sort.dedup.bam', '.metricsdup'])
# Generate chromosome intervals using GATK
pipeline.transform(
task_func=stages.chrom_intervals_gatk,
name='chrom_intervals_gatk',
input=output_from('mark_duplicates_picard'),
filter=suffix('.sort.dedup.bam'),
output='.chr.intervals')
# Local realignment using GATK
(pipeline.transform(
task_func=stages.local_realignment_gatk,
name='local_realignment_gatk',
input=output_from('chrom_intervals_gatk'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).chr.intervals'),
add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.bam'),
output='{path[0]}/{sample[0]}.sort.dedup.realn.bam')
.follows('mark_duplicates_picard'))
# Base recalibration using GATK
pipeline.transform(
task_func=stages.base_recalibration_gatk,
name='base_recalibration_gatk',
input=output_from('local_realignment_gatk'),
filter=suffix('.sort.dedup.realn.bam'),
output=['.recal_data.csv', '.count_cov.log'])
# Print reads using GATK
(pipeline.transform(
task_func=stages.print_reads_gatk,
name='print_reads_gatk',
input=output_from('base_recalibration_gatk'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).recal_data.csv'),
add_inputs=add_inputs('{path[0]}/{sample[0]}.sort.dedup.realn.bam'),
output='{path[0]}/{sample[0]}.sort.dedup.realn.recal.bam')
.follows('local_realignment_gatk'))
# Call variants using GATK
pipeline.transform(
task_func=stages.call_variants_gatk,
name='call_variants_gatk',
input=output_from('print_reads_gatk'),
filter=suffix('.sort.dedup.realn.recal.bam'),
output='.raw.snps.indels.g.vcf')
# Combine G.VCF files for all samples using GATK
pipeline.merge(
task_func=stages.combine_gvcf_gatk,
name='combine_gvcf_gatk',
input=output_from('call_variants_gatk'),
output='PCExomes.mergegvcf.vcf')
# Genotype G.VCF files using GATK
pipeline.transform(
task_func=stages.genotype_gvcf_gatk,
name='genotype_gvcf_gatk',
input=output_from('combine_gvcf_gatk'),
filter=suffix('.mergegvcf.vcf'),
output='.genotyped.vcf')
# SNP recalibration using GATK
pipeline.transform(
task_func=stages.snp_recalibrate_gatk,
name='snp_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
output=['.snp_recal', '.snp_tranches', '.snp_plots.R'])
# INDEL recalibration using GATK
pipeline.transform(
task_func=stages.indel_recalibrate_gatk,
name='indel_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
output=['.indel_recal', '.indel_tranches', '.indel_plots.R'])
# Apply SNP recalibration using GATK
(pipeline.transform(
task_func=stages.apply_snp_recalibrate_gatk,
name='apply_snp_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
add_inputs=add_inputs(['PCExomes.snp_recal', 'PCExomes.snp_tranches']),
output='.recal_SNP.vcf')
.follows('snp_recalibrate_gatk'))
# Apply INDEL recalibration using GATK
(pipeline.transform(
task_func=stages.apply_indel_recalibrate_gatk,
name='apply_indel_recalibrate_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.genotyped.vcf'),
add_inputs=add_inputs(['PCExomes.indel_recal', 'PCExomes.indel_tranches']),
output='.recal_INDEL.vcf')
.follows('indel_recalibrate_gatk'))
# Combine variants using GATK
(pipeline.transform(
task_func=stages.combine_variants_gatk,
name='combine_variants_gatk',
input=output_from('apply_snp_recalibrate_gatk'),
filter=suffix('.recal_SNP.vcf'),
add_inputs=add_inputs(['PCExomes.recal_INDEL.vcf']),
output='.combined.vcf')
.follows('apply_indel_recalibrate_gatk'))
# Select variants using GATK
pipeline.transform(
task_func=stages.select_variants_gatk,
name='select_variants_gatk',
input=output_from('combine_variants_gatk'),
filter=suffix('.combined.vcf'),
output='.selected.vcf')
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='fastq2bam')
# Get a list of paths to all the FASTQ files
input_files = state.config.get_option('files')
# Stages are dependent on the state
stages = Stages(state)
# The original files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_files,
name='original_files',
output=input_files)
#
# performs fastqc on fastq inputs
#
pipeline.transform(
task_func=stages.fastqc,
name='fastqc',
input=output_from('original_files'),
filter=formatter('(?P<path>.+)/(?P<filename>.+).fastq.gz'),
output='{path[0]}/{filename[0]}_fastqc')
#
# converts the fastq inputs to pre-aligned bams
#
pipeline.transform(
task_func=stages.fastq2bam,
name='fastq2bam',
input=output_from('original_files'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+)_R1.fastq.gz'),
add_inputs=add_inputs('{path[0]}/{sample[0]}_R2.fastq.gz'),
extras=['{sample[0]}'],
output='{path[0]}/{sample[0]}.bam')
#
# validates pre-aligned bams x.bam -> x.validation
#
pipeline.transform(
task_func=stages.validate_prealigned_bam,
name='validate_prealigned_bam',
input=output_from('fastq2bam'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).bam'),
output='{path[0]}/{sample[0]}.validation')
# aligns pre-aligned bam x.bam -> x.mapped.bam
pipeline.transform(
task_func=stages.align,
name='align',
input=output_from('validate_prealigned_bam'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).validation'),
add_inputs=add_inputs('{path[0]}/{sample[0]}.bam'),
output='{path[0]}/{sample[0]}.mapped.bam')
# generates stats about an aligned bam
pipeline.transform(
task_func=stages.align_stats_bedtools,
name='align_stats_bedtools',
input=output_from('align'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.genomecov.stats')
# generates stats about an aligned bam
pipeline.transform(
task_func=stages.align_stats_picard,
name='align_stats_picard',
input=output_from('align'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.picard.stats')
#
# runs the Sanger variant calling pipeline
#
#pipeline.transform(
# task_func=stages.analyse_wgs,
# name='analyse_wgs',
# input=output_from('align'),
# filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
# output='{path[0]}/{sample[0]}.wgs/manifest')
# runs the components of the Sanger variant calling pipeline
pipeline.transform(
task_func=stages.analyse_wgs_prepare,
name='analyse_wgs_prepare',
input=output_from('align'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.prepare')
pipeline.transform(
task_func=stages.analyse_wgs_reference_files,
name='analyse_wgs_reference_files',
input=[output_from('align'),
output_from('analyse_wgs_prepare')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.reference_files')
pipeline.transform(
task_func=stages.analyse_wgs_init,
name='analyse_wgs_init',
input=[
output_from('align'),
output_from('analyse_wgs_reference_files')
],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.init')
# block 1
pipeline.transform(
task_func=stages.analyse_wgs_verify_WT,
name='analyse_wgs_verify_WT',
input=[output_from('align'),
output_from('analyse_wgs_init')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.verify_WT')
pipeline.transform(
task_func=stages.analyse_wgs_cgpPindel_input,
name='analyse_wgs_cgpPindel_input',
input=[output_from('align'),
output_from('analyse_wgs_init')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.cgpPindel_input')
pipeline.transform(
task_func=stages.analyse_wgs_alleleCount,
name='analyse_wgs_alleleCount',
input=[output_from('align'),
output_from('analyse_wgs_init')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.alleleCount')
# block 2
pipeline.transform(
task_func=stages.analyse_wgs_ascat,
name='analyse_wgs_ascat',
input=[output_from('align'),
output_from('analyse_wgs_init')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.ascat')
pipeline.transform(
task_func=stages.analyse_wgs_cgpPindel,
name='analyse_wgs_cgpPindel',
input=[
output_from('align'),
output_from('analyse_wgs_cgpPindel_input')
],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.cgpPindel')
pipeline.transform(
task_func=stages.analyse_wgs_BRASS_input,
name='analyse_wgs_BRASS_input',
input=[output_from('align'),
output_from('analyse_wgs_init')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.BRASS_input')
pipeline.transform(
task_func=stages.analyse_wgs_BRASS_cover,
name='analyse_wgs_BRASS_cover',
input=[output_from('align'),
output_from('analyse_wgs_init')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.BRASS_cover')
pipeline.transform(
task_func=stages.analyse_wgs_CaVEMan_split,
name='analyse_wgs_CaVEMan_split',
input=[output_from('align'),
output_from('analyse_wgs_init')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.CaVEMan_split')
# after block 2
pipeline.transform(
task_func=stages.analyse_wgs_ascat_prep,
name='analyse_wgs_ascat_prep',
input=[output_from('align'),
output_from('analyse_wgs_ascat')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.ascat_prep')
pipeline.transform(
task_func=stages.analyse_wgs_pindel_prep,
name='analyse_wgs_pindel_prep',
input=[output_from('align'),
output_from('analyse_wgs_cgpPindel')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.pindel_prep')
# parallel block 3
pipeline.transform(
task_func=stages.analyse_wgs_verify_MT,
name='analyse_wgs_verify_MT',
input=[
output_from('align'),
output_from('analyse_wgs_verify_WT'),
output_from('analyse_wgs_ascat_prep')
],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.verify_MT')
pipeline.transform(
task_func=stages.analyse_wgs_CaVEMan,
name='analyse_wgs_CaVEMan',
input=[
output_from('align'),
output_from('analyse_wgs_CaVEMan_split'),
output_from('analyse_wgs_ascat_prep'),
output_from('analyse_wgs_cgpPindel')
],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.CaVEMan')
pipeline.transform(
task_func=stages.analyse_wgs_BRASS,
name='analyse_wgs_BRASS',
input=[
output_from('align'),
output_from('analyse_wgs_BRASS_cover'),
output_from('analyse_wgs_BRASS_input'),
output_from('analyse_wgs_ascat_prep')
],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.BRASS')
pipeline.transform(
task_func=stages.analyse_wgs_cgpPindel_annot,
name='analyse_wgs_cgpPindel_annot',
input=[output_from('align'),
output_from('analyse_wgs_pindel_prep')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.cgpPindel_annot')
# pre block 4
pipeline.transform(
task_func=stages.analyse_wgs_caveman_prep,
name='analyse_wgs_caveman_prep',
input=[output_from('align'),
output_from('analyse_wgs_CaVEMan')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.caveman_prep')
# block 4
pipeline.transform(
task_func=stages.analyse_wgs_CaVEMan_annot,
name='analyse_wgs_CaVEMan_annot',
input=[output_from('align'),
output_from('analyse_wgs_caveman_prep')],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.CaVEMan_annot')
# done
pipeline.transform(
task_func=stages.analyse_wgs_finish,
name='analyse_wgs_finish',
input=[
output_from('align'),
output_from('analyse_wgs_CaVEMan_annot'),
output_from('analyse_wgs_BRASS'),
output_from('analyse_wgs_cgpPindel_annot'),
output_from('analyse_wgs_alleleCount'),
output_from('analyse_wgs_verify_MT')
],
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.wgs/completed.finish')
#
# runs the delly singularity container
#
pipeline.transform(
task_func=stages.delly,
name='delly',
input=output_from('align'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.delly.completed')
pipeline.transform(
task_func=stages.gridss,
name='gridss',
input=output_from('align'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.gridss.completed')
pipeline.transform(
task_func=stages.muse,
name='muse',
input=output_from('align'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.muse.completed')
pipeline.transform(
task_func=stages.mutect2,
name='mutect2',
input=output_from('align'),
filter=formatter('(?P<path>.+)/(?P<sample>[a-zA-Z0-9]+).mapped.bam'),
output='{path[0]}/{sample[0]}.mutect2.completed')
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='cellfree_seq')
# Stages are dependent on the state
stages = Stages(state)
safe_make_dir('alignments')
# The original FASTQ files
fastq_files = glob.glob('fastqs/*')
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+)_R1.fastq.gz'),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
add_inputs=add_inputs('{path[0]}/{sample[0]}_R2.fastq.gz'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}'],
# The output file name is the sample name with a .bam extension.
output='alignments/{sample[0]}.sort.hq.bam')
pipeline.transform(task_func=stages.run_connor,
name='run_connor',
input=output_from('align_bwa'),
filter=suffix('.sort.hq.bam'),
output='.sort.hq.connor.bam')
safe_make_dir('metrics')
safe_make_dir('metrics/summary')
safe_make_dir('metrics/connor')
pipeline.transform(
task_func=stages.intersect_bed,
name='intersect_bed_raw',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sort.hq.bam'),
output='metrics/summary/{sample[0]}.intersectbed.bam')
pipeline.transform(task_func=stages.coverage_bed,
name='coverage_bed_raw',
input=output_from('intersect_bed_raw'),
filter=suffix('.intersectbed.bam'),
output='.bedtools_hist_all.txt')
pipeline.transform(
task_func=stages.genome_reads,
name='genome_reads_raw',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sort.hq.bam'),
output='metrics/summary/{sample[0]}.mapped_to_genome.txt')
pipeline.transform(task_func=stages.target_reads,
name='target_reads_raw',
input=output_from('intersect_bed_raw'),
filter=suffix('.intersectbed.bam'),
output='.mapped_to_target.txt')
pipeline.transform(
task_func=stages.total_reads,
name='total_reads_raw',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sort.hq.bam'),
output='metrics/summary/{sample[0]}.total_raw_reads.txt')
pipeline.collate(
task_func=stages.generate_stats,
name='generate_stats_raw',
input=output_from('coverage_bed_raw', 'genome_reads_raw',
'target_reads_raw', 'total_reads_raw'),
filter=regex(
r'.+/(.+)\.(bedtools_hist_all|mapped_to_genome|mapped_to_target|total_raw_reads)\.txt'
),
output=r'metrics/summary/all_sample.summary.\1.txt',
extras=[r'\1', 'summary.txt'])
pipeline.transform(
task_func=stages.intersect_bed,
name='intersect_bed_connor',
input=output_from('run_connor'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sort.hq.connor.bam'),
output='metrics/connor/{sample[0]}.intersectbed.bam')
pipeline.transform(task_func=stages.coverage_bed,
name='coverage_bed_connor',
input=output_from('intersect_bed_connor'),
filter=suffix('.intersectbed.bam'),
output='.bedtools_hist_all.txt')
pipeline.transform(
task_func=stages.genome_reads,
name='genome_reads_connor',
input=output_from('run_connor'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sort.hq.connor.bam'),
output='metrics/summary/{sample[0]}.mapped_to_genome.txt')
pipeline.transform(task_func=stages.target_reads,
name='target_reads_connor',
input=output_from('intersect_bed_connor'),
filter=suffix('.intersectbed.bam'),
output='.mapped_to_target.txt')
pipeline.transform(
task_func=stages.total_reads,
name='total_reads_connor',
input=output_from('run_connor'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sort.hq.connor.bam'),
output='metrics/summary/{sample[0]}.total_raw_reads.txt')
pipeline.collate(
task_func=stages.generate_stats,
name='generate_stats_connor',
input=output_from('coverage_bed_connor', 'genome_reads_connor',
'target_reads_connor', 'total_reads_connor'),
filter=regex(
r'.+/(.+)\.(bedtools_hist_all|mapped_to_genome|mapped_to_target|total_raw_reads)\.txt'
),
output=r'metrics/connor/all_sample.summary.\1.txt',
extras=[r'\1', 'connor.summary.txt'])
safe_make_dir('variants')
safe_make_dir('variants/vardict')
pipeline.transform(
task_func=stages.run_vardict,
name='run_vardict',
input=output_from('run_connor'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sort.hq.connor.bam'),
output='variants/vardict/{sample[0]}.vcf',
extras=['{sample[0]}'])
pipeline.transform(
task_func=stages.sort_vcfs,
name='sort_vcfs',
input=output_from('run_vardict'),
filter=formatter('variants/vardict/(?P<sample>[a-zA-Z0-9_-]+).vcf'),
output='variants/vardict/{sample[0]}.sorted.vcf.gz')
pipeline.transform(task_func=stages.index_vcfs,
name='index_vcfs',
input=output_from('sort_vcfs'),
filter=suffix('.sorted.vcf.gz'),
output='.sorted.vcf.gz.tbi')
(pipeline.merge(
task_func=stages.concatenate_vcfs,
name='concatenate_vcfs',
input=output_from('sort_vcfs'),
output='variants/vardict/combined.vcf.gz').follows('index_vcfs'))
pipeline.transform(task_func=stages.vt_decompose_normalise,
name='vt_decompose_normalise',
input=output_from('concatenate_vcfs'),
filter=suffix('.vcf.gz'),
output='.decomp.norm.vcf.gz')
pipeline.transform(task_func=stages.index_vcfs,
name='index_final_vcf',
input=output_from('vt_decompose_normalise'),
filter=suffix('.decomp.norm.vcf.gz'),
output='.decomp.norm.vcf.gz.tbi')
(pipeline.transform(
task_func=stages.apply_vep,
name='apply_vep',
input=output_from('vt_decompose_normalise'),
filter=suffix('.decomp.norm.vcf.gz'),
output='.decomp.norm.vep.vcf').follows('index_final_vcf'))
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='hiplexpipe')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# Hi-Plex example: OHI031002-P02F04_S318_L001_R1_001.fastq
# new sample name = OHI031002-P02F04
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9-]+)_(?P<readid>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_R1_(?P<lib>[a-zA-Z0-9-:]+).fastq'
),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
# Hi-Plex example: OHI031002-P02F04_S318_L001_R2_001.fastq
add_inputs=add_inputs(
'{path[0]}/{sample[0]}_{readid[0]}_{lane[0]}_R2_{lib[0]}.fastq'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}', '{readid[0]}', '{lane[0]}', '{lib[0]}'],
# The output file name is the sample name with a .bam extension.
output='alignments/{sample[0]}_{readid[0]}/{sample[0]}_{readid[0]}.bam'
)
# Call variants using undr_rover
pipeline.transform(
task_func=stages.apply_undr_rover,
name='apply_undr_rover',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9-]+)_(?P<readid>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_R1_(?P<lib>[a-zA-Z0-9-:]+).fastq'
),
add_inputs=add_inputs(
'{path[0]}/{sample[0]}_{readid[0]}_{lane[0]}_R2_{lib[0]}.fastq'),
# extras=['{sample[0]}', '{readid[0]}', '{lane[0]}', '{lib[0]}'],
extras=['{sample[0]}', '{readid[0]}'],
# The output file name is the sample name with a .bam extension.
output='variants/undr_rover/{sample[0]}_{readid[0]}.vcf')
# Sort the BAM file using Picard
pipeline.transform(task_func=stages.sort_bam_picard,
name='sort_bam_picard',
input=output_from('align_bwa'),
filter=suffix('.bam'),
output='.sort.bam')
# High quality and primary alignments
pipeline.transform(task_func=stages.primary_bam,
name='primary_bam',
input=output_from('sort_bam_picard'),
filter=suffix('.sort.bam'),
output='.primary.bam')
# index bam file
pipeline.transform(task_func=stages.index_sort_bam_picard,
name='index_bam',
input=output_from('primary_bam'),
filter=suffix('.primary.bam'),
output='.primary.bam.bai')
# Clip the primer_seq from BAM File
(pipeline.transform(
task_func=stages.clip_bam,
name='clip_bam',
input=output_from('primary_bam'),
filter=suffix('.primary.bam'),
output='.primary.primerclipped.bam').follows('index_bam'))
###### GATK VARIANT CALLING ######
# Call variants using GATK
pipeline.transform(
task_func=stages.call_haplotypecaller_gatk,
name='call_haplotypecaller_gatk',
input=output_from('clip_bam'),
# filter=suffix('.merged.dedup.realn.bam'),
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9-_]+).primary.primerclipped.bam'),
output='variants/gatk/{sample[0]}.g.vcf')
# .follows('index_sort_bam_picard'))
# Combine G.VCF files for all samples using GATK
pipeline.merge(task_func=stages.combine_gvcf_gatk,
name='combine_gvcf_gatk',
input=output_from('call_haplotypecaller_gatk'),
output='variants/gatk/ALL.combined.vcf')
# Genotype G.VCF files using GATK
pipeline.transform(task_func=stages.genotype_gvcf_gatk,
name='genotype_gvcf_gatk',
input=output_from('combine_gvcf_gatk'),
filter=suffix('.combined.vcf'),
output='.raw.vcf')
# Annotate VCF file using GATK
pipeline.transform(task_func=stages.variant_annotator_gatk,
name='variant_annotator_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.raw.vcf'),
output='.raw.annotate.vcf')
# Apply VariantFiltration using GATK
pipeline.transform(task_func=stages.apply_variant_filtration_gatk,
name='apply_variant_filtration_gatk',
input=output_from('variant_annotator_gatk'),
filter=suffix('.raw.annotate.vcf'),
output='.raw.annotate.filtered.vcf')
# Apply NORM
(pipeline.transform(
task_func=stages.apply_vt,
name='apply_vt',
input=output_from('apply_variant_filtration_gatk'),
filter=suffix('.raw.annotate.filtered.vcf'),
# add_inputs=add_inputs(['variants/ALL.indel_recal', 'variants/ALL.indel_tranches']),
output='.raw.annotate.filtered.norm.vcf').follows(
'apply_variant_filtration_gatk'))
# Apply VEP
(pipeline.transform(
task_func=stages.apply_vep,
name='apply_vep',
input=output_from('apply_vt'),
filter=suffix('.raw.annotate.filtered.norm.vcf'),
# add_inputs=add_inputs(['variants/ALL.indel_recal', 'variants/ALL.indel_tranches']),
output='.raw.annotate.filtered.norm.vep.vcf').follows('apply_vt'))
# Apply SnpEff
(pipeline.transform(
task_func=stages.apply_snpeff,
name='apply_snpeff',
input=output_from('apply_vep'),
filter=suffix('.raw.annotate.filtered.norm.vep.vcf'),
# add_inputs=add_inputs(['variants/ALL.indel_recal', 'variants/ALL.indel_tranches']),
output='.raw.annotate.filtered.norm.vep.snpeff.vcf').follows(
'apply_vep'))
# Apply vcfanno
(pipeline.transform(
task_func=stages.apply_vcfanno,
name='apply_vcfanno',
input=output_from('apply_snpeff'),
filter=suffix('.raw.annotate.filtered.norm.vep.snpeff.vcf'),
# add_inputs=add_inputs(['variants/ALL.indel_recal', 'variants/ALL.indel_tranches']),
output='.annotated.vcf').follows('apply_snpeff'))
# Concatenate undr_rover vcf files
pipeline.merge(task_func=stages.apply_cat_vcf,
name='apply_cat_vcf',
input=output_from('apply_undr_rover'),
output='variants/undr_rover/ur.vcf.gz')
# # Apple VEP on concatenated undr_rover vcf file
# (pipeline.transform(
# task_func=stages.apply_vep,
# name='apply_vep_ur',
# input=output_from('apply_cat_vcf'),
# filter=suffix('.vcf.gz'),
# output='.vep.vcf')
# .follows('apply_cat_vcf'))
#
# # Apply vcfanno on concatenated/vep undr_rover vcf file
# (pipeline.transform(
# task_func=stages.apply_vcfanno,
# name='apply_vcfanno_ur',
# input=output_from('apply_vep_ur'),
# filter=suffix('.vep.vcf'),
# output='.vep.anno.vcf')
# .follows('apply_vep_ur'))
#
# # Apply snpeff
# (pipeline.transform(
# task_func=stages.apply_snpeff,
# name='apply_snpeff_ur',
# input=output_from('apply_vcfanno_ur'),
# filter=suffix('.vep.anno.vcf'),
# output='.vep.anno.snpeff.vcf.gz')
# .follows('apply_vcfanno_ur'))
#
# Apply tabix
pipeline.transform(task_func=stages.apply_tabix,
name='apply_tabix',
input=output_from('apply_cat_vcf'),
filter=suffix('.vcf.gz'),
output='.vcf.gz.tbi')
# # Apply HomopolymerRun
# (pipeline.transform(
# task_func=stages.apply_homopolymer_ann,
# name='apply_homopolymer_ann',
# input=output_from('apply_snpeff_ur'),
# filter=suffix('.vep.anno.snpeff.vcf.gz'),
# output='.annotated.vcf')
# .follows('apply_tabix'))
# # Apply summarize multi coverage
# (pipeline.merge(
# task_func=stages.apply_multicov,
# name='apply_multicov',
# input=output_from('primary_bam'),
# # filter=suffix('.primary.bam'),
# output='coverage/all.multicov.txt')
# .follows('index_bam'))
# Apply summarize picard coverage
# (pipeline.merge(
# task_func=stages.apply_summarize_picard,
# name='apply_summarize_picard',
# input=output_from('target_coverage'),
# output='coverage/all.hsmetrics.txt')
# .follows('target_coverage'))
# # Apply summarize multicov coverage plots
# (pipeline.merge(
# task_func=stages.apply_multicov_plots,
# name='apply_multicov_plots',
# input=output_from('apply_multicov'),
# output='coverage/coverage_analysis_main.html')
# .follows('apply_multicov'))
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name="rnapipe")
# Get the details of the experiment (samples, config, inputs, ...)
experiment = Experiment(state)
# Get reference file locations
reference_genome = state.config.get_options("reference_genome")
gene_ref = state.config.get_options("gene_ref")
# Print out samples
sample_text = [s.info() for s in experiment.sample_list]
logging.info("Analysis samples:\n{}".format("\n".join(sample_text)))
# Stages are dependent on the state. Experiment object is also passed so
# we can access metadata later.
stages = PipelineStages(state, experiment=experiment)
# Make directories
output_dir = get_output_paths(
results_dir=state.config.get_options("results_dir"),
default_paths=OUTPUT_PATHS)
make_output_dirs(output_dir)
logging.debug(output_dir)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.do_nothing,
name="original_fastqs",
output=experiment.R1_files)
# Create reference index for alignment
if not experiment.index_provided:
pipeline.originate(task_func=stages.do_nothing,
name="reference_genome",
output=reference_genome)
if experiment.alignment_method == "star":
# Create reference index for STAR
pipeline.transform(task_func=stages.create_star_index,
name="create_star_index",
input=output_from("reference_genome"),
filter=formatter(".*"),
add_inputs=add_inputs(gene_ref),
output=path_list_join(
output_dir["star_index"],
["SA", "Genome", "genomeParameters.txt"]),
extras=[output_dir["star_index"]])
elif experiment.alignment_method == "hisat2":
# Create reference index for HISAT2
hisat_basename = path.join(output_dir["hisat_index"], "genome")
pipeline.transform(
task_func=stages.create_hisat_index,
name="create_hisat_index",
input=output_from("reference_genome"),
filter=formatter(".*"),
add_inputs=add_inputs(gene_ref),
output=path_list_join(output_dir["hisat_index"],
["genome.1.ht2", "genome.2.ht2"]),
extras=[hisat_basename])
else:
# Don't create index if index is supplied
if experiment.alignment_method == "star":
output_dir["star_index"] = state.config.get_options("star_index")
pipeline.originate(task_func=stages.do_nothing,
name="create_star_index",
output=path_list_join(
output_dir["star_index"],
["SA", "Genome", "genomeParameters.txt"]))
elif experiment.alignment_method == "hisat2":
hisat_basename = state.config.get_options("hisat_index")
output_dir["hisat_index"] = path.dirname(hisat_basename)
prefix = path.basename(hisat_basename)
pipeline.originate(task_func=stages.do_nothing,
name="create_hisat_index",
output=path_list_join(
output_dir["hisat_index"], [
"{prefix}.1.ht2".format(prefix=prefix),
"{prefix}.2.ht2".format(prefix=prefix)
]))
# Pre-trim FastQC
if experiment.paired_end:
pipeline.transform(
task_func=stages.fastqc,
name="fastqc",
input=output_from("original_fastqs"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9-_]+)_R1.fastq.gz"),
add_inputs=add_inputs("{path[0]}/{sample[0]}_R2.fastq.gz"),
output=path_list_join(
output_dir["fastqc"],
["{sample[0]}_R1_fastqc.zip", "{sample[0]}_R2_fastqc.zip"]),
extras=[output_dir["fastqc"]])
else:
pipeline.transform(task_func=stages.fastqc,
name="fastqc",
input=output_from("original_fastqs"),
filter=suffix(".fastq.gz"),
output="_fastqc.zip",
output_dir=output_dir["fastqc"],
extras=[output_dir["fastqc"]])
# Trimmomatic
if experiment.trim_reads and experiment.paired_end:
pipeline.transform(
task_func=stages.trim_reads,
name="trim_reads",
input=output_from("original_fastqs"),
# Get R1 file and the corresponding R2 file
filter=formatter(".+/(?P<sample>[a-zA-Z0-9-_]+)_R1.fastq.gz"),
add_inputs=add_inputs("{path[0]}/{sample[0]}_R2.fastq.gz"),
output=path_list_join(output_dir["seq"], [
"{sample[0]}_R1.trimmed.fastq.gz",
"{sample[0]}_R2.trimmed.fastq.gz"
]),
extras=path_list_join(output_dir["seq"], [
"{sample[0]}_R1.unpaired.fastq.gz",
"{sample[0]}_R2.unpaired.fastq.gz"
]))
elif experiment.trim_reads:
pipeline.transform(
task_func=stages.trim_reads,
name="trim_reads",
input=output_from("original_fastqs"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9-_]+)_R1.fastq.gz"),
output=path.join(output_dir["seq"],
"{sample[0]}_R1.trimmed.fastq.gz"))
# Post-trim FastQC
if experiment.paired_end and experiment.trim_reads:
pipeline.transform(
task_func=stages.fastqc,
name="post_trim_fastqc",
input=output_from("trim_reads"),
filter=formatter(
".+/(?P<sample>[a-zA-Z0-9-_]+)_R1.trimmed.fastq.gz"),
output=path_list_join(output_dir["post_trim_fastqc"], [
"{sample[0]}_R1.trimmed_fastqc.gz",
"{sample[0]}_R2.trimmed_fastqc.gz"
]),
extras=["results/qc/post_trim_fastqc/"])
elif experiment.trim_reads:
pipeline.transform(task_func=stages.fastqc,
name="post_trim_fastqc",
input=output_from("trim_reads"),
filter=suffix(".trimmed.fastq.gz"),
output=".trimmed_fastqc.gz",
output_dir=output_dir["post_trim_fastqc"],
extras=[output_dir["post_trim_fastqc"]])
# If there are technical replicates, each is mapped independently.
# This is so each technical replicate maintains a separate read group.
if experiment.alignment_method == "star":
align_task_name = "star_align"
if experiment.trim_reads:
(pipeline.transform(
task_func=stages.star_align,
name=align_task_name,
input=output_from("trim_reads"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9-_]+)" \
"_R[12](.trimmed)?.fastq.gz"),
output="%s/{sample[0]}/{sample[0]}.star.Aligned.out.bam" \
% output_dir["alignments"],
extras=[output_dir["star_index"], "{sample[0]}"])
).follows("create_star_index")
else:
(pipeline.transform(
task_func=stages.star_align,
name=align_task_name,
input=output_from("original_fastqs"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9-_]+)" \
"_R[12](.trimmed)?.fastq.gz"),
output="%s/{sample[0]}/{sample[0]}.star.Aligned.out.bam" \
% output_dir["alignments"],
extras=[output_dir["star_index"], "{sample[0]}"])
).follows("create_star_index")
if experiment.alignment_method == "hisat2":
align_task_name = "hisat_align"
if experiment.trim_reads:
(pipeline.transform(
task_func=stages.hisat_align,
name="hisat_align",
input=output_from("trim_reads"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9-_]+)" \
"_R[12](.trimmed)?.fastq.gz"),
output="%s/{sample[0]}/{sample[0]}.hisat2.bam" \
% output_dir["alignments"],
extras=[hisat_basename, "{sample[0]}"])
).follows("create_hisat_index")
else:
(pipeline.transform(
task_func=stages.hisat_align,
name="hisat_align",
input=output_from("original_fastqs"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9-_]+)" \
"_R[12](.trimmed)?.fastq.gz"),
output="%s/{sample[0]}/{sample[0]}.hisat2.bam" \
% output_dir["alignments"],
extras=[hisat_basename, "{sample[0]}"])
).follows("create_hisat_index")
# Sort BAM by coordinates
pipeline.transform(
task_func=stages.sort_bam_by_coordinate,
name="sort_bam_by_coordinate",
input=output_from(align_task_name),
filter=formatter(
".+/(?P<sample>[a-zA-Z0-9-_]+)\.(?P<method>(star|hisat2))\..*bam"),
output=[
"{path[0]}/{sample[0]}.{method[0]}.sorted.bam",
"{path[0]}/{sample[0]}.{method[0]}.sorted.bam.bai"
])
# Merge files with the same sample name
if experiment.multiple_technical_replicates:
pipeline.collate(
task_func=stages.merge_bams,
name="merge_bams",
input=output_from("sort_bam_by_coordinate"),
filter=formatter(
".+/(SM_)?(?P<sm>[a-zA-Z0-9-]+)[^.]*\.(?P<method>(star|hisat2)).sorted.bam"
),
output=path_list_join(
output_dir["alignments"],
["{sm[0]}.{method[0]}.bam", "{sm[0]}.{method[0]}.bam.bai"]))
else:
pipeline.transform(
task_func=stages.create_symlinks,
name="merge_bams",
input=output_from("sort_bam_by_coordinate"),
filter=formatter(
".+/(SM_)?(?P<sm>[a-zA-Z0-9-]+)[^.]*\.(?P<method>(star|hisat2)).sorted.bam"
),
output=path_list_join(
output_dir["alignments"],
["{sm[0]}.{method[0]}.bam", "{sm[0]}.{method[0]}.bam.bai"]))
# Sort BAM by name for counting features
pipeline.transform(task_func=stages.sort_bam_by_name,
name="sort_bam_by_name",
input=output_from("merge_bams"),
filter=suffix(".bam"),
output=".nameSorted.bam")
# Count features with HTSeq-count
pipeline.transform(task_func=stages.htseq_count,
name="htseq_count",
input=output_from("sort_bam_by_name"),
filter=suffix(".nameSorted.bam"),
output_dir=output_dir["counts"],
output=".htseq.txt")
# Count features with featureCounts
pipeline.transform(task_func=stages.featurecounts,
name="featurecounts",
input=output_from("sort_bam_by_name"),
filter=suffix(".nameSorted.bam"),
output_dir=output_dir["counts"],
output=".featureCounts.txt")
# TODO: add multiqc step
# # Stringtie assembly
# pipeline.transform(
# task_func=stages.stringtie_assembly,
# name="stringtie_assembly",
# input=output_from("merge_bams"),
# filter=suffix(".bam"),
# output_dir=output_dir["stringtie_assembly"],
# output=".gtf")
# Stringtie estimates
pipeline.transform(
task_func=stages.stringtie_estimates,
name="stringtie_estimates",
input=output_from("merge_bams"),
filter=formatter(
".+/(?P<sm>[a-zA-Z0-9-]+)\.(?P<method>(star|hisat2)).bam"),
output=path_list_join(output_dir["stringtie_estimates"],
["{sm[0]}/{sm[0]}.gtf", "{sm[0]}/e_data.ctab"]))
# Stringtie counts
pipeline.collate(
task_func=stages.stringtie_prepDE,
name="stringtie_prepDE",
input=output_from("stringtie_estimates"),
filter=formatter(".+\.gtf"),
output=path_list_join(
output_dir["stringtie_estimates"],
["gene_count_matrix.csv", "transcript_count_matrix.csv"]))
return pipeline
def make_pipeline_map(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='haloplexpipe')
# Get a list of paths to all the FASTQ files
#fastq_files = state.config.get_option('fastqs')
fastq_files = glob.glob("fastqs/*.gz")
# Stages are dependent on the state
stages = Stages(state)
safe_make_dir('alignments')
safe_make_dir('processed_fastqs')
safe_make_dir('metrics')
safe_make_dir('metrics/amplicon')
safe_make_dir('metrics/summary')
safe_make_dir('metrics/pass_samples')
safe_make_dir('variants')
safe_make_dir('variants/gatk')
safe_make_dir('variants/vardict')
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
pipeline.transform(
task_func=stages.run_surecalltrimmer,
name='run_surecalltrimmer',
input=output_from('original_fastqs'),
filter=formatter('fastqs/(?P<sample>[a-zA-Z0-9_-]+)_R1.fastq.gz'),
add_inputs=add_inputs('fastqs/{sample[0]}_R2.fastq.gz'),
#filter=formatter('fastqs/(?P<sample>[a-zA-Z0-9_-]+)_R1_001.fastq.gz'),
#add_inputs=add_inputs('fastqs/{sample[0]}_R3_001.fastq.gz'),
extras=['{sample[0]}'],
# output only needs to know about one file to track progress of the pipeline, but the second certainly exists after this step.
output='processed_fastqs/{sample[0]}_R1.processed.fastq.gz')
#output='processed_fastqs/{sample[0]}_R1_001.processed.fastq.gz')
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('run_surecalltrimmer'),
filter=formatter(
'processed_fastqs/(?P<sample>[a-zA-Z0-9_-]+)_R1.processed.fastq.gz'
),
add_inputs=add_inputs(
'processed_fastqs/{sample[0]}_R2.processed.fastq.gz'),
#filter=formatter('processed_fastqs/(?P<sample>[a-zA-Z0-9_-]+)_R1_001.processed.fastq.gz'),
#add_inputs=add_inputs('processed_fastqs/{sample[0]}_R3_001.processed.fastq.gz'),
extras=['{sample[0]}'],
output='alignments/{sample[0]}.bam')
# Run locatit from agilent. this should produce sorted bam files, so no sorting needed at the next step
pipeline.collate(task_func=stages.run_locatit,
name='run_locatit',
input=output_from('align_bwa', 'original_fastqs'),
filter=regex(r'.+/(.+_L\d\d\d).+'),
output=r'alignments/\1.locatit.bam')
pipeline.transform(task_func=stages.sort_bam,
name='sort_bam',
input=output_from('run_locatit'),
filter=suffix('.locatit.bam'),
output='.sorted.locatit.bam')
# # # # # Metrics stages # # # # #
# generate mapping metrics (post locatit)
pipeline.transform(
task_func=stages.generate_amplicon_metrics,
name='generate_amplicon_metrics',
input=output_from('sort_bam'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sorted.locatit.bam'),
output='metrics/amplicon/{sample[0]}.amplicon-metrics.txt',
extras=['{sample[0]}'])
# Intersect the bam file with the region of interest
pipeline.transform(
task_func=stages.intersect_bed,
name='intersect_bed',
input=output_from('sort_bam'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sorted.locatit.bam'),
output='metrics/summary/{sample[0]}.intersectbed.bam')
# Calculate coverage metrics from the intersected bam file
pipeline.transform(task_func=stages.coverage_bed,
name='coverage_bed',
input=output_from('intersect_bed'),
filter=suffix('.intersectbed.bam'),
output='.bedtools_hist_all.txt')
# Count the number of mapped reads
pipeline.transform(
task_func=stages.genome_reads,
name='genome_reads',
input=output_from('sort_bam'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sorted.locatit.bam'),
output='metrics/summary/{sample[0]}.mapped_to_genome.txt')
# Count the number of on-target reads
pipeline.transform(task_func=stages.target_reads,
name='target_reads',
input=output_from('intersect_bed'),
filter=suffix('.intersectbed.bam'),
output='.mapped_to_target.txt')
# Count the number of total reads
pipeline.transform(
task_func=stages.total_reads,
name='total_reads',
input=output_from('sort_bam'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9_-]+).sorted.locatit.bam'),
output='metrics/summary/{sample[0]}.total_raw_reads.txt')
# Generate summary metrics from the stats files produces
pipeline.collate(
task_func=stages.generate_stats,
name='generate_stats',
input=output_from('coverage_bed', 'genome_reads', 'target_reads',
'total_reads'),
#filter=regex(r'.+/(.+BS\d{4,6}.+S\d+)\..+\.txt'),
filter=regex(
r'.+/(.+)\.(bedtools_hist_all|mapped_to_genome|mapped_to_target|total_raw_reads)\.txt'
),
output=r'metrics/summary/all_sample.summary.\1.txt',
extras=[r'\1', 'all_sample.summary.txt'])
# # # # # Metrics stages end # # # # #
# # # # # Checking metrics and calling # # # # #
# Originate to set the location of the metrics summary file
(pipeline.originate(
task_func=stages.grab_summary_file,
name='grab_summary_file',
output='all_sample.summary.txt').follows('generate_stats'))
# Awk command to produce a list of bam files passing filters
pipeline.transform(task_func=stages.filter_stats,
name='filter_stats',
input=output_from('grab_summary_file'),
filter=suffix('.summary.txt'),
output='.passed.summary.txt')
# Touch passed bams to the pass_samples folder and pass the glob of that folder to HaplotypeCaller
pipeline.subdivide(name='passed_filter_files',
task_func=stages.read_samples,
input=output_from('filter_stats'),
filter=formatter(),
output="metrics/pass_samples/*.bam")
# Call variants using GATK
(pipeline.transform(
task_func=stages.call_haplotypecaller_gatk,
name='call_haplotypecaller_gatk',
input=output_from('passed_filter_files'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9-_]+).sorted.locatit.bam'),
output='variants/gatk/{sample[0]}.g.vcf').follows('sort_bam'))
# Call variants with vardict
(pipeline.transform(
task_func=stages.run_vardict,
name='run_vardict',
input=output_from('passed_filter_files'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9-_]+).sorted.locatit.bam'),
output='variants/vardict/{sample[0]}.vcf',
extras=['{sample[0]}']).follows('sort_bam'))
pipeline.transform(
task_func=stages.sort_vcfs,
name='sort_vcfs',
input=output_from('run_vardict'),
filter=formatter('variants/vardict/(?P<sample>[a-zA-Z0-9_-]+).vcf'),
output='variants/vardict/{sample[0]}.sorted.vcf.gz')
pipeline.transform(task_func=stages.index_vcfs,
name='index_vcfs',
input=output_from('sort_vcfs'),
filter=suffix('.sorted.vcf.gz'),
output='.sorted.vcf.gz.tbi')
return (pipeline)
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='hiplexpipe')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(
task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# Hi-Plex example: OHI031002-P02F04_S318_L001_R1_001.fastq
# new sample name = OHI031002-P02F04
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9-]+)-(?P<tumor>[TN]+)_(?P<readid>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_R1_(?P<lib>[a-zA-Z0-9-:]+).fastq'),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
# Hi-Plex example: OHI031002-P02F04_S318_L001_R2_001.fastq
add_inputs=add_inputs(
'{path[0]}/{sample[0]}-{tumor[0]}_{readid[0]}_{lane[0]}_R2_{lib[0]}.fastq'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}', '{tumor[0]}', '{readid[0]}', '{lane[0]}', '{lib[0]}'],
# The output file name is the sample name with a .bam extension.
output='alignments/{sample[0]}/{sample[0]}_{tumor[0]}.bam')
# Sort the BAM file using Picard
pipeline.transform(
task_func=stages.sort_bam_picard,
name='sort_bam_picard',
input=output_from('align_bwa'),
filter=suffix('.bam'),
output='.sort.bam')
# High quality and primary alignments
pipeline.transform(
task_func=stages.primary_bam,
name='primary_bam',
input=output_from('sort_bam_picard'),
filter=suffix('.sort.bam'),
output='.primary.bam')
# index bam file
pipeline.transform(
task_func=stages.index_sort_bam_picard,
name='index_bam',
input=output_from('primary_bam'),
filter=suffix('.primary.bam'),
output='.primary.bam.bai')
# Clip the primer_seq from BAM File
(pipeline.transform(
task_func=stages.clip_bam,
name='clip_bam',
input=output_from('primary_bam'),
filter=suffix('.primary.bam'),
output='.primary.primerclipped.bam')
.follows('index_bam'))
###### GATK VARIANT CALLING - MuTect2 ######
# Call somatics variants using MuTect2
pipeline.transform(
task_func=stages.call_mutect2_gatk,
name='call_mutect2_gatk',
input=output_from('clip_bam'),
# filter=suffix('.merged.dedup.realn.bam'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9-]+)_T.primary.primerclipped.bam'),
add_inputs=add_inputs(
'{path[0]}/{sample[0]}_N.primary.primerclipped.bam'),
# extras=['{sample[0]}'],
output='variants/mutect2/{sample[0]}.mutect2.vcf')
# .follows('clip_bam')
###### GATK VARIANT CALLING - MuTect2 ######
# -------- VEP ----------
# Apply NORM
(pipeline.transform(
task_func=stages.apply_vt,
name='apply_vt',
input=output_from('call_mutect2_gatk'),
filter=suffix('.mutect2.vcf'),
# add_inputs=add_inputs(['variants/ALL.indel_recal', 'variants/ALL.indel_tranches']),
output='.mutect2.vt.vcf')
.follows('call_mutect2_gatk'))
#
# Apply VEP
(pipeline.transform(
task_func=stages.apply_vep,
name='apply_vep',
input=output_from('apply_vt'),
filter=suffix('.mutect2.vt.vcf'),
# add_inputs=add_inputs(['variants/ALL.indel_recal', 'variants/ALL.indel_tranches']),
output='.mutect2.vt.vep.vcf')
.follows('apply_vt'))
#
# Apply vcfanno
(pipeline.transform(
task_func=stages.apply_vcfanno,
name='apply_vcfanno',
input=output_from('apply_vep'),
filter=suffix('.mutect2.vt.vep.vcf'),
# add_inputs=add_inputs(['variants/ALL.indel_recal', 'variants/ALL.indel_tranches']),
output='.mutect2.annotated.vcf')
.follows('apply_vep'))
return pipeline
def make_pipeline(state):
"""Build the pipeline by constructing stages and connecting them together"""
# Build an empty pipeline
pipeline = Pipeline(name="crpipe")
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option("fastqs")
# Find the path to the reference genome
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs, name="original_fastqs", output=fastq_files)
# Convert FASTQ file to FASTA using fastx toolkit
# pipeline.transform(
# task_func=stages.fastq_to_fasta,
# name='fastq_to_fasta',
# input=output_from('original_fastqs'),
# filter=suffix('.fastq.gz'),
# output='.fasta')
# The original reference file
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
# pipeline.originate(
# task_func=stages.original_reference,
# name='original_reference',
# output=reference_file)
# Run fastQC on the FASTQ files
pipeline.transform(
task_func=stages.fastqc,
name="fastqc",
input=output_from("original_fastqs"),
filter=suffix(".fastq.gz"),
output="_fastqc",
)
# Index the reference using BWA
# pipeline.transform(
# task_func=stages.index_reference_bwa,
# name='index_reference_bwa',
# input=output_from('original_reference'),
# filter=suffix('.fa'),
# output=['.fa.amb', '.fa.ann', '.fa.pac', '.fa.sa', '.fa.bwt'])
# Index the reference using samtools
# pipeline.transform(
# task_func=stages.index_reference_samtools,
# name='index_reference_samtools',
# input=output_from('original_reference'),
# filter=suffix('.fa'),
# output='.fa.fai')
# Index the reference using bowtie 2
# pipeline.transform(
# task_func=stages.index_reference_bowtie2,
# name='index_reference_bowtie2',
# input=output_from('original_reference'),
# filter=formatter('.+/(?P<refname>[a-zA-Z0-9]+\.fa)'),
# output=['{path[0]}/{refname[0]}.1.bt2',
# '{path[0]}/{refname[0]}.2.bt2',
# '{path[0]}/{refname[0]}.3.bt2',
# '{path[0]}/{refname[0]}.4.bt2',
# '{path[0]}/{refname[0]}.rev.1.bt2',
# '{path[0]}/{refname[0]}.rev.2.bt2'],
# extras=['{path[0]}/{refname[0]}'])
# # Create a FASTA sequence dictionary for the reference using picard
# pipeline.transform(
# task_func=stages.reference_dictionary_picard,
# name='reference_dictionary_picard',
# input=output_from('original_reference'),
# filter=suffix('.fa'),
# output='.dict')
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name="align_bwa",
input=output_from("original_fastqs"),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# We assume the sample name may consist of only alphanumeric
# characters.
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+)_R1.fastq.gz"),
# Add two more inputs to the stage:
# 1. The corresponding R2 FASTQ file
add_inputs=add_inputs("{path[0]}/{sample[0]}_R2.fastq.gz"),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=["{sample[0]}"],
# The output file name is the sample name with a .bam extension.
output="{path[0]}/{sample[0]}.bam",
)
# Sort alignment with sambamba
pipeline.transform(
task_func=stages.sort_bam_sambamba,
name="sort_alignment",
input=output_from("align_bwa"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).bam"),
output="{path[0]}/{sample[0]}.sorted.bam",
)
# Extract MMR genes from the sorted BAM file
pipeline.transform(
task_func=stages.extract_genes_bedtools,
name="extract_genes_bedtools",
input=output_from("sort_alignment"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).sorted.bam"),
output="{path[0]}/{sample[0]}.mmr.bam",
)
# Extract selected chromosomes from the sorted BAM file
pipeline.transform(
task_func=stages.extract_chromosomes_samtools,
name="extract_chromosomes_samtools",
input=output_from("sort_alignment"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).sorted.bam"),
output="{path[0]}/{sample[0]}.chroms.bam",
)
# Index the MMR genes bam file with samtools
pipeline.transform(
task_func=stages.index_bam,
name="index_mmr_alignment",
input=output_from("extract_genes_bedtools"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).mmr.bam"),
output="{path[0]}/{sample[0]}.mmr.bam.bai",
)
# Compute depth of coverage of the alignment with GATK DepthOfCoverage
# pipeline.transform(
# task_func=stages.alignment_coverage_gatk,
# name='alignment_coverage_gatk',
# input=output_from('sort_alignment'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
# add_inputs=add_inputs([reference_file]),
# output='{path[0]}/{sample[0]}.coverage_summary',
# extras=['{path[0]}/{sample[0]}_coverage'])
# Index the alignment with samtools
pipeline.transform(
task_func=stages.index_bam,
name="index_alignment",
input=output_from("sort_alignment"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).sorted.bam"),
output="{path[0]}/{sample[0]}.sorted.bam.bai",
)
# Generate alignment stats with bamtools
pipeline.transform(
task_func=stages.bamtools_stats,
name="bamtools_stats",
input=output_from("align_bwa"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).bam"),
output="{path[0]}/{sample[0]}.stats.txt",
)
# Extract the discordant paired-end alignments
pipeline.transform(
task_func=stages.extract_discordant_alignments,
name="extract_discordant_alignments",
input=output_from("align_bwa"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).bam"),
output="{path[0]}/{sample[0]}.discordants.unsorted.bam",
)
# Extract split-read alignments
pipeline.transform(
task_func=stages.extract_split_read_alignments,
name="extract_split_read_alignments",
input=output_from("align_bwa"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).bam"),
output="{path[0]}/{sample[0]}.splitters.unsorted.bam",
)
# Sort discordant reads.
# Samtools annoyingly takes the prefix of the output bam name as its argument.
# So we pass this as an extra argument. However Ruffus needs to know the full name
# of the output bam file, so we pass that as the normal output parameter.
pipeline.transform(
task_func=stages.sort_bam,
name="sort_discordants",
input=output_from("extract_discordant_alignments"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).discordants.unsorted.bam"),
extras=["{path[0]}/{sample[0]}.discordants"],
output="{path[0]}/{sample[0]}.discordants.bam",
)
# Index the sorted discordant bam with samtools
# pipeline.transform(
# task_func=stages.index_bam,
# name='index_discordants',
# input=output_from('sort_discordants'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).discordants.bam'),
# output='{path[0]}/{sample[0]}.discordants.bam.bai')
# Sort discordant reads
# Samtools annoyingly takes the prefix of the output bam name as its argument.
# So we pass this as an extra argument. However Ruffus needs to know the full name
# of the output bam file, so we pass that as the normal output parameter.
pipeline.transform(
task_func=stages.sort_bam,
name="sort_splitters",
input=output_from("extract_split_read_alignments"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).splitters.unsorted.bam"),
extras=["{path[0]}/{sample[0]}.splitters"],
output="{path[0]}/{sample[0]}.splitters.bam",
)
# Index the sorted splitters bam with samtools
# pipeline.transform(
# task_func=stages.index_bam,
# name='index_splitters',
# input=output_from('sort_splitters'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).splitters.bam'),
# output='{path[0]}/{sample[0]}.splitters.bam.bai')
# Call structural variants with lumpy
(
pipeline.transform(
task_func=stages.structural_variants_lumpy,
name="structural_variants_lumpy",
input=output_from("sort_alignment"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).sorted.bam"),
add_inputs=add_inputs(["{path[0]}/{sample[0]}.splitters.bam", "{path[0]}/{sample[0]}.discordants.bam"]),
output="{path[0]}/{sample[0]}.lumpy.vcf",
)
.follows("index_alignment")
.follows("sort_splitters")
.follows("sort_discordants")
)
# Call genotypes on lumpy output using SVTyper
# (pipeline.transform(
# task_func=stages.genotype_svtyper,
# name='genotype_svtyper',
# input=output_from('structural_variants_lumpy'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).lumpy.vcf'),
# add_inputs=add_inputs(['{path[0]}/{sample[0]}.sorted.bam', '{path[0]}/{sample[0]}.splitters.bam']),
# output='{path[0]}/{sample[0]}.svtyper.vcf')
# .follows('align_bwa')
# .follows('sort_splitters')
# .follows('index_alignment')
# .follows('index_splitters')
# .follows('index_discordants'))
# Call SVs with Socrates
(
pipeline.transform(
task_func=stages.structural_variants_socrates,
name="structural_variants_socrates",
input=output_from("sort_alignment"),
filter=formatter(".+/(?P<sample>[a-zA-Z0-9]+).sorted.bam"),
# output goes to {path[0]}/socrates/
output="{path[0]}/socrates/results_Socrates_paired_{sample[0]}.sorted_long_sc_l25_q5_m5_i95.txt",
extras=["{path[0]}"],
)
)
# Call DELs with DELLY
pipeline.merge(
task_func=stages.deletions_delly,
name="deletions_delly",
input=output_from("sort_alignment"),
output="delly.DEL.vcf",
)
# Call DUPs with DELLY
pipeline.merge(
task_func=stages.duplications_delly,
name="duplications_delly",
input=output_from("sort_alignment"),
output="delly.DUP.vcf",
)
# Call INVs with DELLY
pipeline.merge(
task_func=stages.inversions_delly,
name="inversions_delly",
input=output_from("sort_alignment"),
output="delly.INV.vcf",
)
# Call TRAs with DELLY
pipeline.merge(
task_func=stages.translocations_delly,
name="translocations_delly",
input=output_from("sort_alignment"),
output="delly.TRA.vcf",
)
# Join both read pair files using gustaf_mate_joining
# pipeline.transform(
# task_func=stages.gustaf_mate_joining,
# name='gustaf_mate_joining',
# input=output_from('fastq_to_fasta'),
# # Match the R1 (read 1) FASTA file and grab the path and sample name.
# # This will be the first input to the stage.
# # We assume the sample name may consist of only alphanumeric
# # characters.
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+)_R1.fasta'),
# # Add one more input to the stage:
# # 1. The corresponding R2 FASTA file
# add_inputs=add_inputs(['{path[0]}/{sample[0]}_R2.fasta']),
# output='{path[0]}/{sample[0]}.joined_mates.fasta')
# Call structural variants with pindel
# (pipeline.transform(
# task_func=stages.structural_variants_pindel,
# name='structural_variants_pindel',
# input=output_from('sort_alignment'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
# add_inputs=add_inputs(['{path[0]}/{sample[0]}.pindel_config.txt', reference_file]),
# output='{path[0]}/{sample[0]}.pindel')
# .follows('index_reference_bwa')
# .follows('index_reference_samtools'))
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='crpipe')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
# Find the path to the reference genome
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Convert FASTQ file to FASTA using fastx toolkit
# pipeline.transform(
# task_func=stages.fastq_to_fasta,
# name='fastq_to_fasta',
# input=output_from('original_fastqs'),
# filter=suffix('.fastq.gz'),
# output='.fasta')
# The original reference file
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
#pipeline.originate(
# task_func=stages.original_reference,
# name='original_reference',
# output=reference_file)
# Run fastQC on the FASTQ files
pipeline.transform(task_func=stages.fastqc,
name='fastqc',
input=output_from('original_fastqs'),
filter=suffix('.fastq.gz'),
output='_fastqc')
# Index the reference using BWA
#pipeline.transform(
# task_func=stages.index_reference_bwa,
# name='index_reference_bwa',
# input=output_from('original_reference'),
# filter=suffix('.fa'),
# output=['.fa.amb', '.fa.ann', '.fa.pac', '.fa.sa', '.fa.bwt'])
# Index the reference using samtools
# pipeline.transform(
# task_func=stages.index_reference_samtools,
# name='index_reference_samtools',
# input=output_from('original_reference'),
# filter=suffix('.fa'),
# output='.fa.fai')
# Index the reference using bowtie 2
# pipeline.transform(
# task_func=stages.index_reference_bowtie2,
# name='index_reference_bowtie2',
# input=output_from('original_reference'),
# filter=formatter('.+/(?P<refname>[a-zA-Z0-9]+\.fa)'),
# output=['{path[0]}/{refname[0]}.1.bt2',
# '{path[0]}/{refname[0]}.2.bt2',
# '{path[0]}/{refname[0]}.3.bt2',
# '{path[0]}/{refname[0]}.4.bt2',
# '{path[0]}/{refname[0]}.rev.1.bt2',
# '{path[0]}/{refname[0]}.rev.2.bt2'],
# extras=['{path[0]}/{refname[0]}'])
# # Create a FASTA sequence dictionary for the reference using picard
# pipeline.transform(
# task_func=stages.reference_dictionary_picard,
# name='reference_dictionary_picard',
# input=output_from('original_reference'),
# filter=suffix('.fa'),
# output='.dict')
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# We assume the sample name may consist of only alphanumeric
# characters.
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+)_R1.fastq.gz'),
# Add two more inputs to the stage:
# 1. The corresponding R2 FASTQ file
add_inputs=add_inputs('{path[0]}/{sample[0]}_R2.fastq.gz'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}'],
# The output file name is the sample name with a .bam extension.
output='{path[0]}/{sample[0]}.bam')
# Sort alignment with sambamba
pipeline.transform(task_func=stages.sort_bam_sambamba,
name='sort_alignment',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).bam'),
output='{path[0]}/{sample[0]}.sorted.bam')
# Extract MMR genes from the sorted BAM file
pipeline.transform(
task_func=stages.extract_genes_bedtools,
name='extract_genes_bedtools',
input=output_from('sort_alignment'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
output='{path[0]}/{sample[0]}.mmr.bam')
# Extract selected chromosomes from the sorted BAM file
pipeline.transform(
task_func=stages.extract_chromosomes_samtools,
name='extract_chromosomes_samtools',
input=output_from('sort_alignment'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
output='{path[0]}/{sample[0]}.chroms.bam')
# Index the MMR genes bam file with samtools
pipeline.transform(task_func=stages.index_bam,
name='index_mmr_alignment',
input=output_from('extract_genes_bedtools'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).mmr.bam'),
output='{path[0]}/{sample[0]}.mmr.bam.bai')
# Compute depth of coverage of the alignment with GATK DepthOfCoverage
#pipeline.transform(
# task_func=stages.alignment_coverage_gatk,
# name='alignment_coverage_gatk',
# input=output_from('sort_alignment'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
# add_inputs=add_inputs([reference_file]),
# output='{path[0]}/{sample[0]}.coverage_summary',
# extras=['{path[0]}/{sample[0]}_coverage'])
# Index the alignment with samtools
pipeline.transform(
task_func=stages.index_bam,
name='index_alignment',
input=output_from('sort_alignment'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
output='{path[0]}/{sample[0]}.sorted.bam.bai')
# Generate alignment stats with bamtools
pipeline.transform(task_func=stages.bamtools_stats,
name='bamtools_stats',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).bam'),
output='{path[0]}/{sample[0]}.stats.txt')
# Extract the discordant paired-end alignments
pipeline.transform(task_func=stages.extract_discordant_alignments,
name='extract_discordant_alignments',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).bam'),
output='{path[0]}/{sample[0]}.discordants.unsorted.bam')
# Extract split-read alignments
pipeline.transform(task_func=stages.extract_split_read_alignments,
name='extract_split_read_alignments',
input=output_from('align_bwa'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).bam'),
output='{path[0]}/{sample[0]}.splitters.unsorted.bam')
# Sort discordant reads.
# Samtools annoyingly takes the prefix of the output bam name as its argument.
# So we pass this as an extra argument. However Ruffus needs to know the full name
# of the output bam file, so we pass that as the normal output parameter.
pipeline.transform(
task_func=stages.sort_bam,
name='sort_discordants',
input=output_from('extract_discordant_alignments'),
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9]+).discordants.unsorted.bam'),
extras=['{path[0]}/{sample[0]}.discordants'],
output='{path[0]}/{sample[0]}.discordants.bam')
# Index the sorted discordant bam with samtools
# pipeline.transform(
# task_func=stages.index_bam,
# name='index_discordants',
# input=output_from('sort_discordants'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).discordants.bam'),
# output='{path[0]}/{sample[0]}.discordants.bam.bai')
# Sort discordant reads
# Samtools annoyingly takes the prefix of the output bam name as its argument.
# So we pass this as an extra argument. However Ruffus needs to know the full name
# of the output bam file, so we pass that as the normal output parameter.
pipeline.transform(
task_func=stages.sort_bam,
name='sort_splitters',
input=output_from('extract_split_read_alignments'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).splitters.unsorted.bam'),
extras=['{path[0]}/{sample[0]}.splitters'],
output='{path[0]}/{sample[0]}.splitters.bam')
# Index the sorted splitters bam with samtools
# pipeline.transform(
# task_func=stages.index_bam,
# name='index_splitters',
# input=output_from('sort_splitters'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).splitters.bam'),
# output='{path[0]}/{sample[0]}.splitters.bam.bai')
# Call structural variants with lumpy
(pipeline.transform(
task_func=stages.structural_variants_lumpy,
name='structural_variants_lumpy',
input=output_from('sort_alignment'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
add_inputs=add_inputs([
'{path[0]}/{sample[0]}.splitters.bam',
'{path[0]}/{sample[0]}.discordants.bam'
]),
output='{path[0]}/{sample[0]}.lumpy.vcf').follows('index_alignment').
follows('sort_splitters').follows('sort_discordants'))
# Call genotypes on lumpy output using SVTyper
#(pipeline.transform(
# task_func=stages.genotype_svtyper,
# name='genotype_svtyper',
# input=output_from('structural_variants_lumpy'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).lumpy.vcf'),
# add_inputs=add_inputs(['{path[0]}/{sample[0]}.sorted.bam', '{path[0]}/{sample[0]}.splitters.bam']),
# output='{path[0]}/{sample[0]}.svtyper.vcf')
# .follows('align_bwa')
# .follows('sort_splitters')
# .follows('index_alignment')
# .follows('index_splitters')
# .follows('index_discordants'))
# Call SVs with Socrates
(pipeline.transform(
task_func=stages.structural_variants_socrates,
name='structural_variants_socrates',
input=output_from('sort_alignment'),
filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
# output goes to {path[0]}/socrates/
output=
'{path[0]}/socrates/results_Socrates_paired_{sample[0]}.sorted_long_sc_l25_q5_m5_i95.txt',
extras=['{path[0]}']))
# Call DELs with DELLY
pipeline.merge(task_func=stages.deletions_delly,
name='deletions_delly',
input=output_from('sort_alignment'),
output='delly.DEL.vcf')
# Call DUPs with DELLY
pipeline.merge(task_func=stages.duplications_delly,
name='duplications_delly',
input=output_from('sort_alignment'),
output='delly.DUP.vcf')
# Call INVs with DELLY
pipeline.merge(task_func=stages.inversions_delly,
name='inversions_delly',
input=output_from('sort_alignment'),
output='delly.INV.vcf')
# Call TRAs with DELLY
pipeline.merge(task_func=stages.translocations_delly,
name='translocations_delly',
input=output_from('sort_alignment'),
output='delly.TRA.vcf')
# Join both read pair files using gustaf_mate_joining
#pipeline.transform(
# task_func=stages.gustaf_mate_joining,
# name='gustaf_mate_joining',
# input=output_from('fastq_to_fasta'),
# # Match the R1 (read 1) FASTA file and grab the path and sample name.
# # This will be the first input to the stage.
# # We assume the sample name may consist of only alphanumeric
# # characters.
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+)_R1.fasta'),
# # Add one more input to the stage:
# # 1. The corresponding R2 FASTA file
# add_inputs=add_inputs(['{path[0]}/{sample[0]}_R2.fasta']),
# output='{path[0]}/{sample[0]}.joined_mates.fasta')
# Call structural variants with pindel
#(pipeline.transform(
# task_func=stages.structural_variants_pindel,
# name='structural_variants_pindel',
# input=output_from('sort_alignment'),
# filter=formatter('.+/(?P<sample>[a-zA-Z0-9]+).sorted.bam'),
# add_inputs=add_inputs(['{path[0]}/{sample[0]}.pindel_config.txt', reference_file]),
# output='{path[0]}/{sample[0]}.pindel')
# .follows('index_reference_bwa')
# .follows('index_reference_samtools'))
return pipeline
def make_pipeline(state):
'''Build the pipeline by constructing stages and connecting them together'''
# Build an empty pipeline
pipeline = Pipeline(name='hiplexpipe')
# Get a list of paths to all the FASTQ files
fastq_files = state.config.get_option('fastqs')
# Stages are dependent on the state
stages = Stages(state)
# The original FASTQ files
# This is a dummy stage. It is useful because it makes a node in the
# pipeline graph, and gives the pipeline an obvious starting point.
pipeline.originate(task_func=stages.original_fastqs,
name='original_fastqs',
output=fastq_files)
# Align paired end reads in FASTQ to the reference producing a BAM file
pipeline.transform(
task_func=stages.align_bwa,
name='align_bwa',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
# Hi-Plex example: OHI031002-P02F04_S318_L001_R1_001.fastq
# new sample name = OHI031002-P02F04
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9-]+)_(?P<readid>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_R1_(?P<lib>[a-zA-Z0-9-:]+).fastq'
),
# Add one more inputs to the stage:
# 1. The corresponding R2 FASTQ file
# Hi-Plex example: OHI031002-P02F04_S318_L001_R2_001.fastq
add_inputs=add_inputs(
'{path[0]}/{sample[0]}_{readid[0]}_{lane[0]}_R2_{lib[0]}.fastq'),
# Add an "extra" argument to the state (beyond the inputs and outputs)
# which is the sample name. This is needed within the stage for finding out
# sample specific configuration options
extras=['{sample[0]}', '{readid[0]}', '{lane[0]}', '{lib[0]}'],
# The output file name is the sample name with a .bam extension.
output='alignments/{sample[0]}_{readid[0]}/{sample[0]}_{readid[0]}.bam'
)
# Call variants using undr_rover
pipeline.transform(
task_func=stages.apply_undr_rover,
name='apply_undr_rover',
input=output_from('original_fastqs'),
# Match the R1 (read 1) FASTQ file and grab the path and sample name.
# This will be the first input to the stage.
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9-]+)_(?P<readid>[a-zA-Z0-9-]+)_(?P<lane>[a-zA-Z0-9]+)_R1_(?P<lib>[a-zA-Z0-9-:]+).fastq'
),
add_inputs=add_inputs(
'{path[0]}/{sample[0]}_{readid[0]}_{lane[0]}_R2_{lib[0]}.fastq'),
# extras=['{sample[0]}', '{readid[0]}', '{lane[0]}', '{lib[0]}'],
extras=['{sample[0]}', '{readid[0]}'],
# The output file name is the sample name with a .bam extension.
output='variants/undr_rover/{sample[0]}_{readid[0]}.vcf')
# Sort the BAM file using Picard
pipeline.transform(task_func=stages.sort_bam_picard,
name='sort_bam_picard',
input=output_from('align_bwa'),
filter=suffix('.bam'),
output='.sort.bam')
# High quality and primary alignments
pipeline.transform(task_func=stages.primary_bam,
name='primary_bam',
input=output_from('sort_bam_picard'),
filter=suffix('.sort.bam'),
output='.primary.bam')
# index bam file
pipeline.transform(task_func=stages.index_sort_bam_picard,
name='index_bam',
input=output_from('primary_bam'),
filter=suffix('.primary.bam'),
output='.primary.bam.bai')
# Clip the primer_seq from BAM File
(pipeline.transform(
task_func=stages.clip_bam,
name='clip_bam',
input=output_from('primary_bam'),
filter=suffix('.primary.bam'),
output='.primary.primerclipped.bam').follows('index_bam'))
###### GATK VARIANT CALLING ######
# Call variants using GATK
pipeline.transform(
task_func=stages.call_haplotypecaller_gatk,
name='call_haplotypecaller_gatk',
input=output_from('clip_bam'),
# filter=suffix('.merged.dedup.realn.bam'),
filter=formatter(
'.+/(?P<sample>[a-zA-Z0-9-_]+).primary.primerclipped.bam'),
output='variants/gatk/{sample[0]}.g.vcf')
# .follows('index_sort_bam_picard'))
# Combine G.VCF files for all samples using GATK
pipeline.merge(task_func=stages.combine_gvcf_gatk,
name='combine_gvcf_gatk',
input=output_from('call_haplotypecaller_gatk'),
output='variants/gatk/ALL.combined.vcf')
# Genotype G.VCF files using GATK
pipeline.transform(task_func=stages.genotype_gvcf_gatk,
name='genotype_gvcf_gatk',
input=output_from('combine_gvcf_gatk'),
filter=suffix('.combined.vcf'),
output='.raw.vcf')
# Annotate VCF file using GATK
pipeline.transform(task_func=stages.variant_annotator_gatk,
name='variant_annotator_gatk',
input=output_from('genotype_gvcf_gatk'),
filter=suffix('.raw.vcf'),
output='.raw.annotate.vcf')
# Apply VariantFiltration using GATK
pipeline.transform(task_func=stages.apply_variant_filtration_gatk_lenient,
name='apply_variant_filtration_gatk_lenient',
input=output_from('variant_annotator_gatk'),
filter=suffix('.raw.annotate.vcf'),
output='.raw.annotate.filtered_lenient.vcf')
return pipeline
